@article{gao_actigraphy-based_2022,
  title = {Actigraphy-{Based} {Sleep} {Detection}: {Validation} with {Polysomnography} and {Comparison} of {Performance} for {Nighttime} and {Daytime} {Sleep} {During} {Simulated} {Shift} {Work}},
  volume = {14},
  issn = {1179-1608},
  shorttitle = {Actigraphy-{Based} {Sleep} {Detection}},
  url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9581540/},
  doi = {10.2147/NSS.S373107},
  urldate = {2023-02-23},
  journal = {Nature and Science of Sleep},
  author = {Gao, Chenlu and Li, Peng and Morris, Christopher J and Zheng, Xi and Ulsa, Ma Cherrysse and Gao, Lei and Scheer, Frank A J L and Hu, Kun},
  month = oct,
  year = {2022},
  pmid = {36275180},
  pmcid = {PMC9581540},
  pages = {1801--1816},
  file = {PubMed Central Full Text PDF:C\:\\Users\\pl806\\Zotero\\storage\\V92SJJVR\\Gao et al. - 2022 - Actigraphy-Based Sleep Detection Validation with .pdf:application/pdf}
}

Purpose
Actigraphy-based sleep detection algorithms were mostly validated using nighttime sleep, and their performance in detecting daytime sleep is unclear. We evaluated and compared the performance of Actiware and the Cole-Kripke algorithm (C-K) – two commonly used actigraphy-based algorithms – in detecting daytime and nighttime sleep.

Participants and Methods
Twenty-five healthy young adults were monitored by polysomnography and actigraphy during two in-lab protocols with scheduled nighttime and/or daytime sleep (within-subject design). Mixed-effect models were conducted to compare the sensitivity, specificity, and F1 score (a less-biased measure of accuracy) of Actiware (with low/medium/high threshold setting, separately) and C-K in detecting sleep epochs from actigraphy recordings during nighttime/daytime. t-tests and intraclass correlation coefficients were used to assess the agreement between actigraphy-based algorithms and polysomnography in scoring total sleep time (TST).

Results
Sensitivity was similar between nighttime (Actiware: 0.93–0.99 across threshold settings; C-K: 0.61) and daytime sleep (Actiware: 0.93–0.99; C-K: 0.66) for both the C-K and Actiware (daytime/nighttime×algorithm interaction: p \textgreater 0.1). Specificity for daytime sleep was lower (Actiware: 0.35–0.54; C-K: 0.91) than that for nighttime sleep (Actiware: 0.37–0.62; C-K: 0.93; p = 0.001). Specificity was also higher for C-K than Actiware (p \textless 0.001), with no daytime/nighttime×algorithm interaction (p \textgreater 0.1). C-K had lower F1 (nighttime = 0.74; daytime = 0.77) than Actiware (nighttime = 0.95–0.98; daytime = 0.90–0.91) for both nighttime and daytime sleep (all p \textless 0.05). The daytime-nighttime difference in F1 was opposite for Actiware (daytime: 0.90–0.91; nighttime: 0.95–0.98) and C-K (daytime: 0.77; nighttime: 0.74; interaction p = 0.003). Bias in TST was lowest in Actiware (with medium-threshold) for nighttime sleep (underestimation of 5.99 min/8h) and in Actiware (with low-threshold) for daytime sleep (overestimation of 17.75 min/8h).

Conclusion
Daytime/nighttime sleep affected specificity and F1 but not sensitivity of actigraphy-based sleep scoring. Overall, Actiware performed better than the C-K algorithm. Actiware with medium-threshold was the least biased in estimating nighttime TST, and Actiware with low-threshold was the least biased in estimating daytime TST.

@article{gao_sleep_2022,
  title = {Sleep and circadian phenotypes: risk factors for {COVID}-19 severity?},
  volume = {45},
  issn = {1550-9109},
  shorttitle = {Sleep and circadian phenotypes},
  doi = {10.1093/sleep/zsac116},
  language = {eng},
  number = {7},
  journal = {Sleep},
  author = {Gao, Lei and Li, Peng and Lane, Jacqueline M.},
  month = jul,
  year = {2022},
  pmid = {35567789. PMCID: PMC9272288},
  keywords = {Circadian Rhythm, Humans, Sleep, Sleep Disorders, Circadian Rhythm, Risk Factors, Phenotype, COVID-19},
  pages = {zsac116},
  file = {Full Text:C\:\\Users\\pl806\\Zotero\\storage\\VJQNH27L\\Gao et al. - 2022 - Sleep and circadian phenotypes risk factors for C.pdf:application/pdf}
}

@article{yan_entropy_2022,
  title = {Entropy {Analysis} of {Heart} {Rate} {Variability} in {Different} {Sleep} {Stages}},
  volume = {24},
  issn = {1099-4300},
  url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8947316/},
  doi = {10.3390/e24030379},
  number = {3},
  urldate = {2022-09-21},
  journal = {Entropy},
  author = {Yan, Chang and Li, Peng and Yang, Meicheng and Li, Yang and Li, Jianqing and Zhang, Hongxing and Liu, Chengyu},
  month = mar,
  year = {2022},
  pmid = {35327890. PMCID: PMC8947316},
  pages = {379},
  file = {PubMed Central Full Text PDF:C\:\\Users\\pl806\\Zotero\\storage\\MZYAUVEA\\Yan et al. - 2022 - Entropy Analysis of Heart Rate Variability in Diff.pdf:application/pdf}
}

How the complexity or irregularity of heart rate variability (HRV) changes across different sleep stages and the importance of these features in sleep staging are not fully understood. This study aimed to investigate the complexity or irregularity of the RR interval time series in different sleep stages and explore their values in sleep staging. We performed approximate entropy (ApEn), sample entropy (SampEn), fuzzy entropy (FuzzyEn), distribution entropy (DistEn), conditional entropy (CE), and permutation entropy (PermEn) analyses on RR interval time series extracted from epochs that were constructed based on two methods: (1) 270-s epoch length and (2) 300-s epoch length. To test whether adding the entropy measures can improve the accuracy of sleep staging using linear HRV indices, XGBoost was used to examine the abilities to differentiate among: (i) 5 classes [Wake (W), non-rapid-eye-movement (NREM), which can be divide into 3 sub-stages: stage N1, stage N2, and stage N3, and rapid-eye-movement (REM)]; (ii) 4 classes [W, light sleep (combined N1 and N2), deep sleep (N3), and REM]; and (iii) 3 classes: (W, NREM, and REM). SampEn, FuzzyEn, and CE significantly increased from W to N3 and decreased in REM. DistEn increased from W to N1, decreased in N2, and further decreased in N3; it increased in REM. The average accuracy of the three tasks using linear and entropy features were 42.1%, 59.1%, and 60.8%, respectively, based on 270-s epoch length; all were significantly lower than the performance based on 300-s epoch length (i.e., 54.3%, 63.1%, and 67.5%, respectively). Adding entropy measures to the XGBoost model of linear parameters did not significantly improve the classification performance. However, entropy measures, especially PermEn, DistEn, and FuzzyEn, demonstrated greater importance than most of the linear parameters in the XGBoost model.300-s270-s.

@article{ulsa_association_2022,
  title = {Association of {Poor} {Sleep} {Burden} in {Middle} {Age} and {Older} {Adults} {With} {Risk} for {Delirium} {During} {Hospitalization}},
  volume = {77},
  issn = {1758-535X},
  doi = {10.1093/gerona/glab272},
  language = {eng},
  number = {3},
  journal = {The Journals of Gerontology. Series A, Biological Sciences and Medical Sciences},
  author = {Ulsa, Ma Cherrysse and Xi, Zheng and Li, Peng and Gaba, Arlen and Wong, Patricia M. and Saxena, Richa and Scheer, Frank A. J. L. and Rutter, Martin and Akeju, Oluwaseun and Hu, Kun and Gao, Lei},
  month = mar,
  year = {2022},
  pmid = {34558609},
  pmcid = {PMC8893188},
  keywords = {Chronotype, Circadian rhythms, Napping, Perioperative neurocognitive disorders, Sleep health},
  pages = {507--516},
  file = {Full Text:C\:\\Users\\pl806\\Zotero\\storage\\K3L6FPCP\\Ulsa et al. - 2022 - Association of Poor Sleep Burden in Middle Age and.pdf:application/pdf}
}

BACKGROUND: Delirium is a distressing neurocognitive disorder recently linked to sleep disturbances. However, the longitudinal relationship between sleep and delirium remains unclear. This study assessed the associations of poor sleep burden, and its trajectory, with delirium risk during hospitalization.
METHODS: About 321 818 participants from the UK Biobank (mean age 58 ± 8 years [SD]; range 37-74 years) reported (2006-2010) sleep traits (sleep duration, excessive daytime sleepiness, insomnia-type complaints, napping, and chronotype-a closely related circadian measure for sleep timing), aggregated into a sleep burden score (0-9). New-onset delirium (n = 4 775) was obtained from hospitalization records during a 12-year median follow-up. About 42 291 (mean age 64 ± 8 years; range 44-83 years) had repeat sleep assessment on average 8 years after their first.
RESULTS: In the baseline cohort, Cox proportional hazards models showed that moderate (aggregate scores = 4-5) and severe (scores = 6-9) poor sleep burden groups were 18% (hazard ratio = 1.18 [95% confidence interval: 1.08-1.28], p \textless .001) and 57% (1.57 [1.38-1.80], p \textless .001), more likely to develop delirium, respectively. The latter risk magnitude is equivalent to 2 additional cardiovascular risks. These findings appeared robust when restricted to postoperative delirium and after exclusion of underlying dementia. Higher sleep burden was also associated with delirium in the follow-up cohort. Worsening sleep burden (score increase ≥2 vs no change) further increased the risk for delirium (1.79 [1.23-2.62], p = .002) independent of their baseline sleep score and time lag. The risk was highest in those younger than 65 years at baseline (p for interaction \textless.001).
CONCLUSION: Poor sleep burden and worsening trajectory were associated with increased risk for delirium; promotion of sleep health may be important for those at higher risk.

@article{yan_entropy_2023,
  title = {Entropy {Analysis} of {Heart} {Rate} {Variability} in {Different} {Sleep} {Stages}},
  volume = {24},
  issn = {1099-4300},
  url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8947316/},
  doi = {10.3390/e24030379},
  number = {3},
  urldate = {2022-09-21},
  journal = {Entropy},
  author = {Yan, Chang and Li, Peng and Yang, Meicheng and Li, Yang and Li, Jianqing and Zhang, Hongxing and Liu, Chengyu},
  month = mar,
  year = {2022},
  pmid = {35327890. PMCID: PMC8947316},
  pages = {379},
  file = {PubMed Central Full Text PDF:C\:\\Users\\pl806\\Zotero\\storage\\MZYAUVEA\\Yan et al. - 2022 - Entropy Analysis of Heart Rate Variability in Diff.pdf:application/pdf}
}

How the complexity or irregularity of heart rate variability (HRV) changes across different sleep stages and the importance of these features in sleep staging are not fully understood. This study aimed to investigate the complexity or irregularity of the RR interval time series in different sleep stages and explore their values in sleep staging. We performed approximate entropy (ApEn), sample entropy (SampEn), fuzzy entropy (FuzzyEn), distribution entropy (DistEn), conditional entropy (CE), and permutation entropy (PermEn) analyses on RR interval time series extracted from epochs that were constructed based on two methods: (1) 270-s epoch length and (2) 300-s epoch length. To test whether adding the entropy measures can improve the accuracy of sleep staging using linear HRV indices, XGBoost was used to examine the abilities to differentiate among: (i) 5 classes [Wake (W), non-rapid-eye-movement (NREM), which can be divide into 3 sub-stages: stage N1, stage N2, and stage N3, and rapid-eye-movement (REM)]; (ii) 4 classes [W, light sleep (combined N1 and N2), deep sleep (N3), and REM]; and (iii) 3 classes: (W, NREM, and REM). SampEn, FuzzyEn, and CE significantly increased from W to N3 and decreased in REM. DistEn increased from W to N1, decreased in N2, and further decreased in N3; it increased in REM. The average accuracy of the three tasks using linear and entropy features were 42.1%, 59.1%, and 60.8%, respectively, based on 270-s epoch length; all were significantly lower than the performance based on 300-s epoch length (i.e., 54.3%, 63.1%, and 67.5%, respectively). Adding entropy measures to the XGBoost model of linear parameters did not significantly improve the classification performance. However, entropy measures, especially PermEn, DistEn, and FuzzyEn, demonstrated greater importance than most of the linear parameters in the XGBoost model.300-s270-s.

@article{qian_unanticipated_2022,
  title = {Unanticipated daytime melatonin secretion on a simulated night shift schedule generates a distinctive 24-h melatonin rhythm with antiphasic daytime and nighttime peaks},
  volume = {72},
  issn = {1600-079X},
  url = {https://onlinelibrary.wiley.com/doi/abs/10.1111/jpi.12791},
  doi = {10.1111/jpi.12791},
  language = {en},
  number = {3},
  urldate = {2022-03-15},
  journal = {Journal of Pineal Research},
  author = {Qian, Jingyi and Morris, Christopher J. and Phillips, Andrew J. K. and Li, Peng and Rahman, Shadab A. and Wang, Wei and Hu, Kun and Arendt, Josephine and Czeisler, Charles A. and Scheer, Frank A. J. L.},
  year = {2022},
  pmid = {35133678},
  keywords = {glucose metabolism, circadian pacemaker, melatonin, night shift},
  pages = {e12791},
  file = {Snapshot:C\:\\Users\\pl806\\Zotero\\storage\\RT4UNGPU\\jpi.html:text/html}
}

The daily rhythm of plasma melatonin concentrations is typically unimodal, with one broad peak during the circadian night and near-undetectable levels during the circadian day. Light at night acutely suppresses melatonin secretion and phase shifts its endogenous circadian rhythm. In contrast, exposure to darkness during the circadian day has not generally been reported to increase circulating melatonin concentrations acutely. Here, in a highly-controlled simulated night shift protocol with 12-h inverted behavioral/environmental cycles, we unexpectedly found that circulating melatonin levels were significantly increased during daytime sleep (p \textless .0001). This resulted in a secondary melatonin peak during the circadian day in addition to the primary peak during the circadian night, when sleep occurred during the circadian day following an overnight shift. This distinctive diurnal melatonin rhythm with antiphasic peaks could not be readily anticipated from the behavioral/environmental factors in the protocol (e.g., light exposure, posture, diet, activity) or from current mathematical model simulations of circadian pacemaker output. The observation, therefore, challenges our current understanding of underlying physiological mechanisms that regulate melatonin secretion. Interestingly, the increase in melatonin concentration observed during daytime sleep was positively correlated with the change in timing of melatonin nighttime peak (p = .002), but not with the degree of light-induced melatonin suppression during nighttime wakefulness (p = .92). Both the increase in daytime melatonin concentrations and the change in the timing of the nighttime peak became larger after repeated exposure to simulated night shifts (p = .002 and p = .006, respectively). Furthermore, we found that melatonin secretion during daytime sleep was positively associated with an increase in 24-h glucose and insulin levels during the night shift protocol (p = .014 and p = .027, respectively). Future studies are needed to elucidate the key factor(s) driving the unexpected daytime melatonin secretion and the melatonin rhythm with antiphasic peaks during shifted sleep/wake schedules, the underlying mechanisms of their relationship with glucose metabolism, and the relevance for diabetes risk among shift workers.

@article{li_daytime_2022,
  title = {Daytime {Sleep} {Behaviors} and {Cognitive} {Performance} in {Middle}- to {Older}-{Aged} {Adults} {Living} with and without {HIV} {Infection}},
  volume = {14},
  issn = {1179-1608},
  doi = {10.2147/NSS.S339230},
  language = {eng},
  journal = {Nature and Science of Sleep},
  author = {Li, Peng and Gao, Lei and Gao, Chenlu and Parker, Robert A. and Katz, Ingrid T. and Montano, Monty A. and Hu, Kun},
  year = {2022},
  pmid = {35173500},
  pmcid = {PMC8843344},
  keywords = {aging, sleep, cognition, daytime napping, daytime sleepiness, risk factors},
  pages = {181--191},
  file = {Full Text:C\:\\Users\\pl806\\Zotero\\storage\\VEMD9999\\Li et al. - 2022 - Daytime Sleep Behaviors and Cognitive Performance .pdf:application/pdf}
}

Purpose: We investigated whether daytime sleep behaviors (DSBs) such as frequent daytime sleepiness or napping are associated with worse cognitive performance, and whether HIV infection moderates this relationship.
Methods: Among 502,507 participants in the UK Biobank study, we identified 562 people living with HIV infection (PLWH; M age= 50.51±7.81; 25.09% female; 78.83% white) and extracted 562 uninfected controls who matched on age, sex, ethnic background, social-economic status, and comorbidities. DSB burden was assessed based on answers to two questions on DSBs. Participants who answered "sometimes" or "often/usually" to one of them were considered to have poor DSB burden, or otherwise were considered not having any. A composite cognition score was computed by averaging the available standardized individual test results from four neurocognitive tests: ie, a reaction time test for information processing speed, a pairs matching test for visual episodic memory, a fluid intelligence test for reasoning, and a prospective memory test. Mixed-effects models with adjustment for the variables used in extracting matched uninfected controls were performed to test the hypotheses.
Results: Having poor DSB burden was associated with a 0.15 - standard deviation (SD) decrease in cognitive performance (p = 0.006). People living with HIV infection (PLWH) also performed worse on the cognitive tasks than uninfected controls, with an effect size similar to that of having poor DSB burden (p = 0.003). HIV infection significantly modified the negative association between DSB burden and cognition (p for interaction: 0.008). Specifically, the association between DSB burden and cognition was not statistically significant in uninfected controls, whereas PLWH who reported having poor DSB burden had a 0.28 - SD decrease in cognitive performance compared to PLWH who did not.
Conclusion: HIV infection significantly increased the adverse association between DSBs and cognitive performance. Further studies are needed to investigate the potential mechanisms that underlie this interaction effect and whether poor DSBs and worse cognitive performance are causally linked.

@article{li_integrating_2021,
  title = {Integrating multi-domain deep features of electrocardiogram and phonocardiogram for coronary artery disease detection},
  volume = {138},
  issn = {0010-4825},
  url = {https://www.sciencedirect.com/science/article/pii/S0010482521007083},
  doi = {10.1016/j.compbiomed.2021.104914},
  language = {en},
  urldate = {2021-10-18},
  journal = {Computers in Biology and Medicine},
  author = {Li, Han and Wang, Xinpei and Liu, Changchun and Li, Peng and Jiao, Yu},
  month = nov,
  year = {2021},
  pmid = {34638021},
  keywords = {ECG, Deep learning, Coronary artery disease, Multi-domain features, PCG},
  pages = {104914},
  file = {ScienceDirect Snapshot:C\:\\Users\\pl806\\Zotero\\storage\\3PI3AC8K\\S0010482521007083.html:text/html}
}

Electrocardiogram (ECG) and phonocardiogram (PCG) are both noninvasive and convenient tools that can capture abnormal heart states caused by coronary artery disease (CAD). However, it is very challenging to detect CAD relying on ECG or PCG alone due to low diagnostic sensitivity. Recently, several studies have attempted to combine ECG and PCG signals for diagnosing heart abnormalities, but only conventional manual features have been used. Considering the strong feature extraction capabilities of deep learning, this paper develops a multi-input convolutional neural network (CNN) framework that integrates time, frequency, and time-frequency domain deep features of ECG and PCG for CAD detection. Simultaneously recorded ECG and PCG signals from 195 subjects are used. The proposed framework consists of 1-D and 2-D CNN models and uses signals, spectrum images, and time-frequency images of ECG and PCG as inputs. The framework combining multi-domain deep features of two-modal signals is very effective in classifying non-CAD and CAD subjects, achieving an accuracy, sensitivity, and specificity of 96.51%, 99.37%, and 90.08%, respectively. The comparison with existing studies demonstrates that our method is very competitive in CAD detection. The proposed approach is very promising in assisting the real-world CAD diagnosis, especially under general medical conditions.

@article{hu_sleep_2021,
  title = {Sleep, rest-activity rhythms and aging: a complex web in {Alzheimer}'s disease?},
  volume = {104},
  issn = {0197-4580},
  shorttitle = {Sleep, rest-activity rhythms and aging},
  doi = {10.1016/j.neurobiolaging.2021.02.017},
  language = {en},
  urldate = {2021-07-18},
  journal = {Neurobiology of Aging},
  author = {Hu, Kun and Li, Peng and Gao, Lei},
  month = aug,
  year = {2021},
  pmid = {33902941},
  pages = {102--103},
  file = {ScienceDirect Full Text PDF:C\:\\Users\\pl806\\Zotero\\storage\\24X69SQE\\Hu et al. - 2021 - Sleep, rest-activity rhythms and aging a complex .pdf:application/pdf;ScienceDirect Snapshot:C\:\\Users\\pl806\\Zotero\\storage\\NFNVSF9T\\S0197458021000725.html:text/html}
}

@article{yang_daily_2021,
  title = {Daily {Rhythm} of {Fractal} {Cardiac} {Dynamics} {Links} to {Weight} {Loss} {Resistance}: {Interaction} with {CLOCK} {3111T}/{C} {Genetic} {Variant}},
  volume = {13},
  copyright = {http://creativecommons.org/licenses/by/3.0/},
  shorttitle = {Daily {Rhythm} of {Fractal} {Cardiac} {Dynamics} {Links} to {Weight} {Loss} {Resistance}},
  url = {https://www.mdpi.com/2072-6643/13/7/2463},
  doi = {10.3390/nu13072463},
  language = {en},
  number = {7},
  urldate = {2021-07-19},
  journal = {Nutrients},
  author = {Yang, Hui-Wen and Garaulet, Marta and Li, Peng and Bandin, Cristina and Lin, Chen and Lo, Men-Tzung and Hu, Kun},
  month = jul,
  year = {2021},
  note = {Number: 7
  Publisher: Multidisciplinary Digital Publishing Institute},
  keywords = {circadian rhythm, autonomic function, dietary intervention, fractal cardiac dynamics, obesity treatment, weight control},
  pages = {2463},
  file = {Full Text PDF:C\:\\Users\\pl806\\Zotero\\storage\\D7KANATC\\Yang et al. - 2021 - Daily Rhythm of Fractal Cardiac Dynamics Links to .pdf:application/pdf;Snapshot:C\:\\Users\\pl806\\Zotero\\storage\\K9VXUTBY\\2463.html:text/html}
}

The effectiveness of weight loss treatment displays dramatic inter-individual variabilities, even with well-controlled energy intake/expenditure. This study aimed to determine the association between daily rhythms of cardiac autonomic control and weight loss efficiency and to explore the potential relevance to weight loss resistance in humans carrying the genetic variant C at CLOCK 3111T/C. A total of 39 overweight/obese Caucasian women (20 CLOCK 3111C carriers and 19 non-carriers) completed a behaviour–dietary obesity treatment of ~20 weeks, during which body weight was assessed weekly. Ambulatory electrocardiographic data were continuously collected for up to 3.5 days and used to quantify the daily rhythm of fractal cardiac dynamics (FCD), a non-linear measure of autonomic function. FCD showed a 24 h rhythm (p < 0.001). Independent of energy intake and physical activity level, faster weight loss was observed in individuals with the phase (peak) of the rhythm between ~2–8 p.m. and with a larger amplitude. Interestingly, the phase effect was significant only in C carriers (p = 0.008), while the amplitude effect was only significant in TT carriers (p < 0.0001). The daily rhythm of FCD and CLOCK 3111T/C genotype is linked to weight loss response interactively, suggesting complex interactions between the genetics of the circadian clock, the daily rhythm of autonomic control, and energy balance control.

@article{knapen_fractal_2021,
  title = {Fractal biomarker of activity in patients with bipolar disorder},
  volume = {51},
  issn = {1469-8978},
  doi = {10.1017/S0033291720000331},
  language = {eng},
  number = {9},
  journal = {Psychological Medicine},
  author = {Knapen, Stefan E. and Li, Peng and Riemersma-van der Lek, Rixt F. and Verkooijen, Sanne and Boks, Marco P. M. and Schoevers, Robert A. and Scheer, Frank A. J. L. and Hu, Kun},
  month = jul,
  year = {2021},
  pmid = {32234100},
  keywords = {Actigraphy, scale invariance, bipolar disorder, mood disorder, fractal patterns, sleep–wake rhythm},
  pages = {1562--1569}
}

BACKGROUND: The output of many healthy physiological systems displays fractal fluctuations with self-similar temporal structures. Altered fractal patterns are associated with pathological conditions. There is evidence that patients with bipolar disorder have altered daily behaviors.
METHODS: To test whether fractal patterns in motor activity are altered in patients with bipolar disorder, we analyzed 2-week actigraphy data collected from 106 patients with bipolar disorder type I in a euthymic state, 73 unaffected siblings of patients, and 76 controls. To examine the link between fractal patterns and symptoms, we analyzed 180-day actigraphy and mood symptom data that were simultaneously collected from 14 patients.
RESULTS: Compared to controls, patients showed excessive regularity in motor activity fluctuations at small time scales (\textless1.5 h) as quantified by a larger scaling exponent (α1 \textgreater 1), indicating a more rigid motor control system. α1 values of siblings were between those of patients and controls. Further examinations revealed that the group differences in α1 were only significant in females. Sex also affected the group differences in fractal patterns at larger time scales (\textgreater2 h) as quantified by scaling exponent α2. Specifically, female patients and siblings had a smaller α2 compared to female controls, indicating more random activity fluctuations; while male patients had a larger α2 compared to male controls. Interestingly, a higher weekly depression score was associated with a lower α1 in the subsequent week.
CONCLUSIONS: Our results show sex- and scale-dependent alterations in fractal activity regulation in patients with bipolar disorder. The mechanisms underlying the alterations are yet to be determined.

@article{wang_association_2021,
  title = {Association between short-term heart rate variability and blood coagulation in patients with breast cancer},
  volume = {11},
  issn = {2045-2322},
  doi = {10.1038/s41598-021-94931-w},
  language = {eng},
  number = {1},
  journal = {Scientific Reports},
  author = {Wang, Lingling and Wang, Jingfeng and Li, Peng and Wang, Xiangzhi and Wu, Shuang and Shi, Bo},
  month = jul,
  year = {2021},
  pmid = {34326419. PMCID: PMC8322388},
  pages = {15414},
  file = {Full Text:C\:\\Users\\pl806\\Zotero\\storage\\NKBVCZSB\\Wang et al. - 2021 - Association between short-term heart rate variabil.pdf:application/pdf}
}

The purpose of this study was to investigate the relationship between heart rate variability (HRV), a non-invasive tool for evaluating autonomic function, and routine coagulation indices (RCIs) in patients with breast cancer (BC). Forty-six BC patients were enrolled in this study. Blood biochemistry tests were performed to extract RCIs, including prothrombin time (PT), activated partial thromboplastin time (APTT), and thrombin time (TT). Five-minute electrocardiograms were collected for analysis of HRV parameters (SDNN, RMSSD, LF, HF, LF n.u., HF n.u., LF/HF). Multiple linear regression models examined the relationship of HRV parameters with RCIs. RMSSD, LF n.u., HF n.u., LF/HF were significantly associated with PT. Specifically, the value of PT increased by 0.192 ± 0.091 or 0.231 ± 0.088 s, respectively for each 1 standard deviation (SD) increase in RMSSD or HF n.u.; it increased by 0.230 ± 0.088 or 0.215 ± 0.088 s, respectively for each 1 - SD decrease in LF n.u. or ln (LF/HF) (all P \textless 0.05). RMSSD was significantly associated with APTT, i.e., the value of APTT increased by 1.032 ± 0.470 s for each 1 - SD increase in RMSSD (P \textless 0.05). HRV parameters were associated with RCIs in patients with BC. These observations suggest that the autonomic nervous system and coagulation indices in BC patients are linked, potentially explaining the reason that they are both associated with the prognosis.

@article{li_objective_2021,
  title = {Objective {Assessment} of {Daytime} {Napping} and {Incident} {Heart} {Failure} in 1140 {Community}‐{Dwelling} {Older} {Adults}: {A} {Prospective}, {Observational} {Cohort} {Study}},
  volume = {10},
  shorttitle = {Objective {Assessment} of {Daytime} {Napping} and {Incident} {Heart} {Failure} in 1140 {Community}‐{Dwelling} {Older} {Adults}},
  url = {https://www.ahajournals.org/doi/full/10.1161/JAHA.120.019037},
  doi = {10.1161/JAHA.120.019037},
  number = {12},
  urldate = {2021-07-18},
  journal = {Journal of the American Heart Association},
  author = {Li, Peng and Gaba, Arlen and Wong, Patricia M. and Cui, Longchang and Yu, Lei and Bennett, David A. and Buchman, Aron S. and Gao, Lei and Hu, Kun},
  month = jun,
  year = {2021},
  pmid = {34075783},
  keywords = {actigraphy, sleep, cardiovascular disease, mobile health, unobtrusive monitoring, wearables},
  pages = {e019037},
  file = {Full Text PDF:C\:\\Users\\pl806\\Zotero\\storage\\KRL5Y6SJ\\Li et al. - 2021 - Objective Assessment of Daytime Napping and Incide.pdf:application/pdf}
}

Background

Disrupted nighttime sleep has been associated with heart failure (HF). However, the relationship between daytime napping, an important aspect of sleep behavior commonly seen in older adults, and HF remains unclear. We sought to investigate the association of objectively assessed daytime napping and risk of incident HF during follow‐up.

Methods and Results

We studied 1140 older adults (age, 80.7±7.4 [SD] years; female sex, 867 [76.1%]) in the Rush Memory and Aging Project who had no HF at baseline and were followed annually for up to 14 years. Motor activity (ie, actigraphy) was recorded for ≈10 days at baseline. We assessed daytime napping episodes between 9 am and 7 pm objectively from actigraphy using a previously published algorithm for sleep detection. Cox proportional hazards models examined associations of daily napping duration and frequency with incident HF. Eighty‐six participants developed incident HF, and the mean onset time was 5.7 years (SD, 3.4; range, 1–14). Participants who napped longer than 44.4 minutes (ie, the median daily napping duration) showed a 1.73‐fold higher risk of developing incident HF than participants who napped \textless44.4 minutes. Consistently, participants who napped \textgreater1.7 times/day (ie, the median daily napping frequency) showed a 2.20‐fold increase compared with participants who napped \textless1.7 times/day. These associations persisted after adjustment for covariates, including nighttime sleep, comorbidities, and cardiovascular disease/risk factors.

Conclusions

Longer and more frequent objective napping predicted elevated future risk of developing incident HF. Future studies are needed to establish underlying mechanisms.

@article{li_poor_2021,
  title = {Poor sleep behavior burden and risk of {COVID}-19 mortality and hospitalization},
  volume = {44},
  issn = {0161-8105},
  doi = {10.1093/sleep/zsab138},
  number = {8},
  urldate = {2021-07-09},
  journal = {Sleep},
  author = {Li, Peng and Zheng, Xi and Ulsa, Ma Cherrysse and Yang, Hui-Wen and Scheer, Frank A J L and Rutter, Martin K and Hu, Kun and Gao, Lei},
  month = jun,
  year = {2021},
  pmid = {34142713},
  pages = {zsab138},
  file = {Full Text PDF:C\:\\Users\\pl806\\Zotero\\storage\\B4KDT8PF\\Li et al. - 2021 - Poor sleep behavior burden and risk of COVID-19 mo.pdf:application/pdf;Snapshot:C\:\\Users\\pl806\\Zotero\\storage\\PNVXUU6B\\6304657.html:text/html}
}

Dear Editor,Despite the unprecedented efforts in vaccinations, the COVID-19 pandemic will likely continue to affect those most vulnerable [1], and the need to identify modifiable risk factors remains [2]. Poor sleep behavior traits have been linked to multisystemic disruptions [3], but whether this is linked to COVID-19 severity remains unknown. Using, the UK Biobank, a population-based cohort of >500 000 participants recruited between 2006 and 2010 [4], we determined the associations of poor sleep behavior burden with mortality, and need for hospitalization after a positive COVID-19 result.

@article{zhao_influence_2021,
  title = {Influence of {Ectopic} {Beats} on {Heart} {Rate} {Variability} {Analysis}},
  volume = {23},
  issn = {1099-4300},
  doi = {10.3390/e23060648},
  language = {eng},
  number = {6},
  journal = {Entropy (Basel, Switzerland)},
  author = {Zhao, Lina and Li, Peng and Li, Jianqing and Liu, Chengyu},
  month = may,
  year = {2021},
  pmid = {34067255. PMCID: PMC8224602},
  keywords = {electrocardiogram, heart rate variability, sample entropy, congestive heart failure},
  pages = {648},
  file = {Full Text:C\:\\Users\\pl806\\Zotero\\storage\\W8HALEV6\\Zhao et al. - 2021 - Influence of Ectopic Beats on Heart Rate Variabili.pdf:application/pdf}
}

The analysis of heart rate variability (HRV) plays a dominant role in the study of physiological signal variability. HRV reflects the information of the adjustment of sympathetic and parasympathetic nerves on the cardiovascular system and, thus, is widely used to evaluate the functional status of the cardiovascular system. Ectopic beats may affect the analysis of HRV. However, the quantitative relationship between the burden of ectopic beats and HRV indices, including entropy measures, has not yet been investigated in depth. In this work, we analyzed the effects of different numbers of ectopic beats on several widely accepted HRV parameters in time-domain (SDNN), frequency-domain (LF/HF), as well as non-linear features (SampEn and Pt-SampEn (physical threshold-based SampEn)). The results showed that all four indices were influenced by ectopic beats, and the degree of influence was roughly increased with the increase of the number of ectopic beats. Ectopic beats had the greatest impact on the frequency domain index LF/HF, whereas the Pt-SampEn was minimally accepted by ectopic beats. These results also indicated that, compared with the other three indices, Pt-SampEn had better robustness for ectopic beats.

@article{liu_detection_2021,
  title = {Detection of {Coronary} {Artery} {Disease} {Using} {Multi}-{Domain} {Feature} {Fusion} of {Multi}-{Channel} {Heart} {Sound} {Signals}},
  volume = {23},
  issn = {1099-4300},
  doi = {10.3390/e23060642},
  language = {eng},
  number = {6},
  journal = {Entropy (Basel, Switzerland)},
  author = {Liu, Tongtong and Li, Peng and Liu, Yuanyuan and Zhang, Huan and Li, Yuanyang and Jiao, Yu and Liu, Changchun and Karmakar, Chandan and Liang, Xiaohong and Ren, Mengli and Wang, Xinpei},
  month = may,
  year = {2021},
  pmid = {34064025. PMCID: PMC8224099},
  keywords = {entropy, coronary artery disease, cross entropy, heart sound, multi-channel},
  pages = {642},
  file = {Full Text:C\:\\Users\\pl806\\Zotero\\storage\\Q5GSH7MM\\Liu et al. - 2021 - Detection of Coronary Artery Disease Using Multi-D.pdf:application/pdf}
}

Heart sound signals reflect valuable information about heart condition. Previous studies have suggested that the information contained in single-channel heart sound signals can be used to detect coronary artery disease (CAD). But accuracy based on single-channel heart sound signal is not satisfactory. This paper proposed a method based on multi-domain feature fusion of multi-channel heart sound signals, in which entropy features and cross entropy features are also included. A total of 36 subjects enrolled in the data collection, including 21 CAD patients and 15 non-CAD subjects. For each subject, five-channel heart sound signals were recorded synchronously for 5 min. After data segmentation and quality evaluation, 553 samples were left in the CAD group and 438 samples in the non-CAD group. The time-domain, frequency-domain, entropy, and cross entropy features were extracted. After feature selection, the optimal feature set was fed into the support vector machine for classification. The results showed that from single-channel to multi-channel, the classification accuracy has increased from 78.75% to 86.70%. After adding entropy features and cross entropy features, the classification accuracy continued to increase to 90.92%. The study indicated that the method based on multi-domain feature fusion of multi-channel heart sound signals could provide more information for CAD detection, and entropy features and cross entropy features played an important role in it.

@article{farina_ecoacoustics_2021,
  title = {Ecoacoustics and {Multispecies} {Semiosis}: {Naming}, {Semantics}, {Semiotic} {Characteristics}, and {Competencies}},
  volume = {14},
  issn = {1875-1350},
  shorttitle = {Ecoacoustics and {Multispecies} {Semiosis}},
  url = {https://doi.org/10.1007/s12304-021-09402-6},
  doi = {10.1007/s12304-021-09402-6},
  language = {en},
  number = {1},
  urldate = {2021-06-15},
  journal = {Biosemiotics},
  author = {Farina, Almo and Eldridge, Alice and Li, Peng},
  month = apr,
  year = {2021},
  pages = {141--165},
  file = {Springer Full Text PDF:C\:\\Users\\pl806\\Zotero\\storage\\325BQW55\\Farina et al. - 2021 - Ecoacoustics and Multispecies Semiosis Naming, Se.pdf:application/pdf}
}

Biosemiotics to date has focused on the exchange of signals between organisms, in line with bioacoustics; consideration of the wider acoustic environment as a semiotic medium is under-developed. The nascent discipline of ecoacoustics, that investigates the role of environmental sound in ecological processes and dynamics, fills this gap. In this paper we introduce key ecoacoustic terminology and concepts in order to highlight the value of ecoacoustics as a discipline in which to conceptualise and study intra- and interspecies semiosis. We stress the inherently subjective nature of all sensory scapes (vivo-, land-, vibro- and soundscapes) and propose that they should always bear an organismic attribution. Key terms to describe the sources (geophony, biophony, anthropophony, technophony) and scales (sonotopes, soundtopes, sonotones) of soundscapes are described. We introduce epithets for soundscapes to point to the degree to which the global environment is implicated in semiosis (latent, sensed and interpreted soundscapes); terms for describing key ecological structures and processes (acoustic community, acoustic habitat, ecoacoustic events) and examples of ecoacoustic events (choruses and noise) are described. The acoustic eco-field is recognized as the semiotic model that enables soniferous species to intercept core resources like food, safety and roosting places. We note that whilst ecoacoustics to date has focused on the critical task of the development of metrics for application in conservation and biodiversity assessment, these can be enriched by advancing conceptual and theoretical foundations. Finally, the mutual value of integrating ecoacoustic and biosemiotics perspectives is considered.

@article{yan_short-term_2021,
  title = {Short-{Term} {Effect} of {Percutaneous} {Coronary} {Intervention} on {Heart} {Rate} {Variability} in {Patients} with {Coronary} {Artery} {Disease}},
  volume = {23},
  issn = {1099-4300},
  doi = {10.3390/e23050540},
  language = {eng},
  number = {5},
  journal = {Entropy (Basel, Switzerland)},
  author = {Yan, Chang and Liu, Changchun and Yao, Lianke and Wang, Xinpei and Wang, Jikuo and Li, Peng},
  month = apr,
  year = {2021},
  pmid = {33924819. PMCID: PMC8146536},
  keywords = {autonomic function, complexity, area index (AI), heart rate asymmetry (HRA)},
  pages = {540},
  file = {Full Text:C\:\\Users\\pl806\\Zotero\\storage\\N5V4NHK8\\Yan et al. - 2021 - Short-Term Effect of Percutaneous Coronary Interve.pdf:application/pdf}
}

Myocardial ischemia in patients with coronary artery disease (CAD) leads to imbalanced autonomic control that increases the risk of morbidity and mortality. To systematically examine how autonomic function responds to percutaneous coronary intervention (PCI) treatment, we analyzed data of 27 CAD patients who had admitted for PCI in this pilot study. For each patient, five-minute resting electrocardiogram (ECG) signals were collected before and after the PCI procedure. The time intervals between ECG collection and PCI were both within 24 h. To assess autonomic function, normal sinus RR intervals were extracted and were analyzed quantitatively using traditional linear time- and frequency-domain measures [i.e., standard deviation of the normal-normal intervals (SDNN), the root mean square of successive differences (RMSSD), powers of low frequency (LF) and high frequency (HF) components, LF/HF] and nonlinear entropy measures [i.e., sample entropy (SampEn), distribution entropy (DistEn), and conditional entropy (CE)], as well as graphical metrics derived from Poincaré plot [i.e., Porta’s index (PI), Guzik’s index (GI), slope index (SI) and area index (AI)]. Results showed that after PCI, AI and PI decreased significantly (p \textless 0.002 and 0.015, respectively) with effect sizes of 0.88 and 0.70 as measured by Cohen’s d static. These changes were independent of sex. The results suggest that graphical AI and PI metrics derived from Poincaré plot of short-term ECG may be potential for sensing the beneficial effect of PCI on cardiovascular autonomic control. Further studies with bigger sample sizes are warranted to verify these observations.

@article{gao_resting_2021,
  title = {Resting {Heartbeat} {Complexity} {Predicts} {All}‐{Cause} and {Cardiorespiratory} {Mortality} in {Middle}‐ to {Older}‐{Aged} {Adults} {From} the {UK} {Biobank}},
  volume = {10},
  url = {https://www.ahajournals.org/doi/10.1161/JAHA.120.018483},
  doi = {10.1161/JAHA.120.018483},
  number = {3},
  urldate = {2021-02-25},
  journal = {Journal of the American Heart Association},
  author = {Gao, Lei and Gaba, Arlen and Cui, Longchang and Yang, Hui-Wen and Saxena, Richa and Scheer, Frank A. J. L. and Akeju, Oluwaseun and Rutter, Martin K. and Lo, Men‐Tzung and Hu, Kun and Li, Peng},
  month = feb,
  year = {2021},
  pmid = {33461311},
  pages = {e018483},
  file = {Full Text PDF:C\:\\Users\\pl806\\Zotero\\storage\\A8QMN6WN\\Gao Lei et al. - 2021 - Resting Heartbeat Complexity Predicts All‐Cause an.pdf:application/pdf;Snapshot:C\:\\Users\\pl806\\Zotero\\storage\\QU767VBX\\JAHA.120.html:text/html}
}

BackgroundSpontaneous heart rate fluctuations contain rich information related to health and illness in terms of physiological complexity, an accepted indicator of plasticity and adaptability. However, it is challenging to make inferences on complexity from shorter, more practical epochs of data. Distribution entropy (DistEn) is a recently introduced complexity measure that is designed specifically for shorter duration heartbeat recordings. We hypothesized that reduced DistEn predicted increased mortality in a large population cohort.Method and ResultsThe prognostic value of DistEn was examined in 7631 middle‐older–aged UK Biobank participants who had 2‐minute resting ECGs conducted (mean age, 59.5 years; 60.4% women). During a median follow‐up period of 7.8 years, 451 (5.9%) participants died. In Cox proportional hazards models with adjustment for demographics, lifestyle factors, physical activity, cardiovascular risks, and comorbidities, for each 1‐SD decrease in DistEn, the risk increased by 36%, 56%, and 73% for all‐cause, cardiovascular, and respiratory disease–related mortality, respectively. These effect sizes were equivalent to the risk of death from being \textgreater5 years older, having been a former smoker, or having diabetes mellitus. Lower DistEn was most predictive of death in those \textless55 years with a prior myocardial infarction, representing an additional 56% risk for mortality compared with older participants without prior myocardial infarction. These observations remained after controlling for traditional mortality predictors, resting heart rate, and heart rate variability.ConclusionsResting heartbeat complexity from short, resting ECGs was independently associated with mortality in middle‐ to older‐aged adults. These risks appear most pronounced in middle‐aged participants with prior MI, and may uniquely contribute to mortality risk screening.

@article{farina_acoustic_2021,
  title = {Acoustic complexity indices reveal the acoustic communities of the old-growth {Mediterranean} forest of {Sasso} {Fratino} {Integral} {Natural} {Reserve} ({Central} {Italy})},
  volume = {120},
  issn = {1470-160X},
  url = {http://www.sciencedirect.com/science/article/pii/S1470160X20308669},
  doi = {10.1016/j.ecolind.2020.106927},
  language = {en},
  urldate = {2020-09-21},
  journal = {Ecological Indicators},
  author = {Farina, A. and Righini, R. and Fuller, S. and Li, P. and Pavan, G.},
  month = jan,
  year = {2021},
  keywords = {Fractal dimension, Acoustic events, Acoustic signature, Acoustic signature dissimilarity, Old-growth Mediterranean forest},
  pages = {106927},
  file = {ScienceDirect Full Text PDF:C\:\\Users\\pl806\\Zotero\\storage\\2JMJSJZZ\\Farina et al. - 2021 - Acoustic complexity indices reveal the acoustic co.pdf:application/pdf;ScienceDirect Snapshot:C\:\\Users\\pl806\\Zotero\\storage\\VTKG8U96\\S1470160X20308669.html:text/html}
}

The Sasso Fratino Integral Natural Reserve (Central Italy), a rare example of climax Mediterranean forest, provides an extraordinary opportunity to create an important soundscape reference of old-growth forest. In this study, we describe the soundscape of three localities (Lama, Sasso 950, Sasso 1400) representative of a gradient of variety and complexity of habitats, recorded during the period 10 May to 9 June 2017. Our results reveal temporal partitioning into acoustically homogeneous periods across 24 h suggesting that soniferous species (mainly birds) adopt ecological routines in which their acoustic activity is organized according to specific transient physiological needs. We processed multi-temporal aggregates of 1, 5, 10, and 15 s recordings and calculated the Acoustic Signature (AS) with four new indices: Ecoacoustic Events (EE), Acoustic Signature Dissimilarity (ASD), and their fractal dimensions (DEE and DASD), derived from the Acoustic Complexity Index (ACI). The use of the EE and ASD greatly improved the AS interpretation, adding further details such as the emergence of a clear sequence of patterns consistent with the daily evolution of the overall soundscape. DEE and DASD confirm the patterns observed using the AS, but provide more clarity and detail about the great acoustic complexity that exists across temporal scales in this old-growth forest. The temporal turnover of different acoustic communities occurs as a result of a gradual shift of different homogenous acoustic properties. We conclude that soniferous species use distinct, species-specific temporal resolutions according to their physiological and ecological needs and that the fractal approach used here provides a novel tool to overcome the difficulties associated with describing multi-temporal acoustic patterns.

@article{gao_fractal_2021,
  title = {Fractal motor activity regulation and sex differences in preclinical {Alzheimer}'s disease pathology},
  volume = {13},
  issn = {2352-8729},
  url = {https://alz-journals.onlinelibrary.wiley.com/doi/abs/10.1002/dad2.12211},
  doi = {10.1002/dad2.12211},
  language = {en},
  number = {1},
  urldate = {2021-07-09},
  journal = {Alzheimer's \& Dementia: Diagnosis, Assessment \& Disease Monitoring},
  author = {Gao, Lei and Li, Peng and Gaba, Arlen and Musiek, Erik and Ju, Yo-El S. and Hu, Kun},
  year = {2021},
  pmid = {34189248. PMCID: PMC8220856},
  keywords = {actigraphy, fractal regulation, sex differences, amyloid beta 42, amyloid plaque pathology, amyloid positron emission tomography imaging, interdaily stability, intradaily variability, phosphorylated tau, Pittsburgh compound B, preclinical Alzheimer's disease},
  pages = {e12211},
  file = {Full Text PDF:C\:\\Users\\pl806\\Zotero\\storage\\FDTYM55H\\Gao et al. - 2021 - Fractal motor activity regulation and sex differen.pdf:application/pdf;Snapshot:C\:\\Users\\pl806\\Zotero\\storage\\YY8B4Y8P\\dad2.html:text/html}
}

Introduction Degradation in fractal motor activity regulation (FMAR), a measure of multiscale self-similarity of motor control, occurs in aging and accelerates with clinical progression to Alzheimer’s disease (AD). Whether FMAR changes occur during the pre-symptomatic phase of the disease in women and men remains unknown. Methods FMAR was assessed in cognitively normal participants (n = 178) who underwent 7 to 14 days of home actigraphy. Preclinical AD pathology was determined by amyloid imaging-Pittsburgh compound B (PiB) and cerebrospinal fluid (CSF) phosphorylated-tau181 (p-tau) to amyloid beta 42 (Aβ42) ratio. Results Degradation in daytime FMAR was overall significantly associated with preclinical amyloid plaque pathology via PiB+ imaging (beta coefficient β = 0.217, standard error [SE] = 0.101, P = .034) and increasing CSF tau181-Aβ42 ratio (β = 0.220, SE = 0.084, P = .009). In subset analysis by sex, the effect sizes were significant in women for PiB+ (β = 0.279, SE = 0.112, P = .015) and CSF (β = 0.245, SE = 0.094, P = .011) but not in men (both Ps \textgreater .05). These associations remained after inclusion of daily activity level, apolipoprotein E ε4 carrier status, and rest/activity patterns. Discussion Changes in daytime FMAR from actigraphy appear to be present in women early in preclinical AD. This may be a combination of earlier pathology changes in females reflected in daytime FMAR, and a relatively underpowered male group. Further studies are warranted to test FMAR as an early noncognitive physiological biomarker that precedes the onset of cognitive symptoms.

@article{farina_perspectives_2021,
  title = {Perspectives on the {Ecological} {Role} of {Geophysical} {Sounds}},
  volume = {9},
  issn = {2296-701X},
  url = {https://www.frontiersin.org/article/10.3389/fevo.2021.748398},
  doi = {10.3389/fevo.2021.748398},
  urldate = {2021-12-21},
  journal = {Frontiers in Ecology and Evolution},
  author = {Farina, Almo and Mullet, Tim C. and Bazarbayeva, Tursynkul A. and Tazhibayeva, Tamara and Bulatova, Diana and Li, Peng},
  year = {2021},
  pages = {919},
  file = {Full Text PDF:C\:\\Users\\pl806\\Zotero\\storage\\2ZQRGPQ4\\Farina et al. - 2021 - Perspectives on the Ecological Role of Geophysical.pdf:application/pdf}
}

Humans categorize unwanted sounds in the environment as noise. Consequently, noise is associated with negative human and ecological values, especially when it is derived from an anthropogenic source. Although evidence confirms that many machine-generated anthropogenic sounds have negative impacts on animal behavior and communication, natural sources of non-biological sound, such as wind, rain, running water, and sea waves (geophonies) have also been categorized as noise and are frequently dismissed or mischaracterized in acoustic studies as an outside factor of acoustic habitats rather than an integrated sonic component of ecological processes and species adaptations. While the proliferation of machine-generated sound in the Biosphere has become an intrusive phenomenon in recent history, geophony has shaped the Earth’s sonic landscapes for billions of years. Therefore, geophonies have very important sonic implications to the evolution and adaptation of soniferous species, forming essential ecological and semiotical relationships. This creates a need to distinguish geophonies from machine-generated sounds and how species respond to each accordingly, especially given their acoustic similarities in the frequency spectrum. Here, we introduce concepts and terminology that address these differences in the context of ecoacoustics. We also discuss how Acoustic Complexity Indices (ACIs) can offer new possibilities to quantifiably evaluate geophony in relation to their sonic contest.

@article{li_circadian_2020,
  title = {Circadian disturbances in {Alzheimer}'s disease progression: a prospective observational cohort study of community-based older adults},
  volume = {1},
  issn = {2666-7568},
  shorttitle = {Circadian disturbances in {Alzheimer}'s disease progression},
  url = {https://www.sciencedirect.com/science/article/pii/S2666756820300155},
  doi = {10.1016/S2666-7568(20)30015-5},
  language = {en},
  number = {3},
  urldate = {2021-02-19},
  journal = {The Lancet Healthy Longevity},
  author = {Li, Peng and Gao, Lei and Gaba, Arlen and Yu, Lei and Cui, Longchang and Fan, Wenqing and Lim, Andrew S P and Bennett, David A and Buchman, Aron S and Hu, Kun},
  month = dec,
  year = {2020},
  pmid = {34179863},
  pmcid = {PMC8232345},
  pages = {e96--e105},
  file = {ScienceDirect Full Text PDF:C\:\\Users\\pl806\\Zotero\\storage\\PICVVJ7U\\Li et al. - 2020 - Circadian disturbances in Alzheimer's disease prog.pdf:application/pdf;ScienceDirect Snapshot:C\:\\Users\\pl806\\Zotero\\storage\\9NFYPDQA\\S2666756820300155.html:text/html}
}

Background
Circadian disturbances are commonly seen in people with Alzheimer’s disease and have been reported in individuals without symptoms of dementia but with Alzheimer’s pathology. We aimed to assess the temporal relationship between circadian disturbances and Alzheimer’s progression.
Methods
We did a prospective cohort study of 1401 healthy older adults (aged \textgreater59 years) enrolled in the Rush Memory and Aging Project (Rush University Medical Center, Chicago, IL, USA) who had been followed up for up to 15 years. Participants underwent annual assessments of cognition (with a battery of 21 cognitive performance tests) and motor activities (with actigraphy). Four measures were extracted from actigraphy to quantify daily and circadian rhythmicity, which were amplitude of 24-h activity rhythm, acrophase (representing peak activity time), interdaily stability of 24-h activity rhythm, and intradaily variability for hourly fragmentation of activity rhythm. We used Cox proportional hazards models and logistic regressions to assess whether circadian disturbances predict an increased risk of incident Alzheimer’s dementia and conversion of mild cognitive impairment to Alzheimer’s dementia. We used linear mixed-effects models to investigate how circadian rhythms changed longitudinally and how the change integrated to Alzheimer’s progression.
Findings
Participants had a median age of 81·8 (IQR 76·3–85·7) years. Risk of developing Alzheimer’s dementia was increased with lower amplitude (1 SD decrease, hazard ratio [HR] 1·39, 95% CI 1·19–1·62) and higher intradaily variability (1 SD increase, 1·22, 1·04–1·43). In participants with mild cognitive impairment, increased risk of Alzheimer’s dementia was predicted by lower amplitude (1 SD decrease, HR 1·46, 95% CI 1·24–1·72), higher intradaily variability (1 SD increase, 1·36, 1·15–1·60), and lower interdaily stability (1 SD decrease, 1·21, 1·02–1·44). A faster transition to Alzheimer’s dementia in participants with mild cognitive impairment was predicted by lower amplitude (1 SD decrease, odds ratio [OR] 2·08, 95% CI 1·53–2·93), increased intradaily variability (1 SD increase, 1·97, 1·43–2·79), and decreased interdaily stability (1 SD decrease, 1·35, 1·01–1·84). Circadian amplitude, acrophase, and interdaily stability progressively decreased over time, and intradaily variability progressively increased over time. Alzheimer’s progression accelerated these aging effects by doubling or more than doubling the annual changes in these measures after the diagnosis of mild cognitive impairment, and further doubled them after the diagnosis of Alzheimer’s dementia. The longitudinal change of global cognition positively correlated with the longitudinal changes in amplitude and interdaily stability and negatively correlated with the longitudinal change in intradaily variability.
Interpretation
Our results indicate a link between circadian dysregulation and Alzheimer’s progression, implying either a bidirectional relation or shared common underlying pathophysiological mechanisms.
Funding
National Institutes of Health, and the BrightFocus Foundation.

@article{shi_bivariate_2020,
  title = {Bivariate {Entropy} {Analysis} of {Electrocardiographic} {RR}–{QT} {Time} {Series}},
  volume = {22},
  copyright = {http://creativecommons.org/licenses/by/3.0/},
  url = {https://www.mdpi.com/1099-4300/22/12/1439},
  doi = {10.3390/e22121439},
  language = {en},
  number = {12},
  urldate = {2021-01-12},
  journal = {Entropy},
  author = {Shi, Bo and Motin, Mohammod Abdul and Wang, Xinpei and Karmakar, Chandan and Li, Peng},
  month = dec,
  year = {2020},
  pmid = {33419293. PMCID: PMC7766536},
  keywords = {ambulatory monitoring, cross entropy, joint distribution entropy, RR–QT relationship},
  pages = {1439},
  file = {Full Text PDF:C\:\\Users\\pl806\\Zotero\\storage\\JRJMYPRW\\Shi et al. - 2020 - Bivariate Entropy Analysis of Electrocardiographic.pdf:application/pdf;Snapshot:C\:\\Users\\pl806\\Zotero\\storage\\VYK92Q2V\\1439.html:text/html}
}

QT interval variability (QTV) and heart rate variability (HRV) are both accepted biomarkers for cardiovascular events. QTV characterizes the variations in ventricular depolarization and repolarization. It is a predominant element of HRV. However, QTV is also believed to accept direct inputs from upstream control system. How QTV varies along with HRV is yet to be elucidated. We studied the dynamic relationship of QTV and HRV during different physiological conditions from resting, to cycling, and to recovering. We applied several entropy-based measures to examine their bivariate relationships, including cross sample entropy (XSampEn), cross fuzzy entropy (XFuzzyEn), cross conditional entropy (XCE), and joint distribution entropy (JDistEn). Results showed no statistically significant differences in XSampEn, XFuzzyEn, and XCE across different physiological states. Interestingly, JDistEn demonstrated significant decreases during cycling as compared with that during the resting state. Besides, JDistEn also showed a progressively recovering trend from cycling to the first 3 min during recovering, and further to the second 3 min during recovering. It appeared to be fully recovered to its level in the resting state during the second 3 min during the recovering phase. The results suggest that there is certain nonlinear temporal relationship between QTV and HRV, and that the JDistEn could help unravel this nuanced property.

@article{zhang_detection_2020,
  title = {Detection of coronary artery disease using multi-modal feature fusion and hybrid feature selection},
  volume = {41},
  issn = {0967-3334},
  url = {https://doi.org/10.1088/1361-6579/abc323},
  doi = {10.1088/1361-6579/abc323},
  language = {en},
  number = {11},
  urldate = {2021-04-07},
  journal = {Physiological Measurement},
  author = {Zhang, Huan and Wang, Xinpei and Liu, Changchun and Liu, Yuanyuan and Li, Peng and Yao, Lianke and Li, Han and Wang, Jikuo and Jiao, Yu},
  month = nov,
  year = {2020},
  pmid = {33080588},
  pages = {115007}
}

Objective: Coronary artery disease (CAD) is a common fatal disease. At present, an accurate method to screen CAD is urgently needed. This study aims to provide optimal detection models for suspected CAD detection according to the differences in medical conditions, so as to assist physicians to make accurate judgments on suspected CAD patients. Approach: Electrocardiogram (ECG) and phonocardiogram (PCG) signals of 32 CAD patients and 30 patients with chest pain and normal coronary angiograms (CPNCA) were simultaneously collected for this paper. For each subject, the ECG and PCG multi-domain features were extracted, and the results of Holter monitoring, echocardiography (ECHO), and biomarker levels (BIO) were obtained to construct a multi-modal feature set. Then, a hybrid feature selection (HFS) method was developed using mutual information, recursive feature elimination, random forest, and weight of support vector machine to obtain the optimal feature subset. A support vector machine with nested cross-validation was used for classification. Main results: Results showed that the Holter model achieved the best performance as a single-modal feature model with an accuracy of 82.67%. In terms of multi-modal feature models, PCG-Holter, PCG-Holter-ECHO, PCG-Holter-ECHO-BIO, and ECG-PCG-Holter-ECHO-BIO were the optimal bimodal, three-modal, four-modal, and five-modal models, with accuracies of 90.38%, 91.92%, 95.25%, and 96.67%, respectively. Among them, the ECG-PCG-Holter-ECHO-BIO model, which was constructed by combining ECG and PCG signals features with Holter, ECHO, and BIO examination results, achieved the best classification results with an average accuracy, sensitivity, specificity, and F1-measure of 96.67%, 96.67%, 96.67%, and 96.64%, respectively. Significance: The study indicated that multi-modal feature fusion and HFS can obtain more effective information for CAD detection and provide a reference for physicians to diagnose CAD patients.

@article{udhayakumar_modified_2020,
  title = {Modified {Distribution} {Entropy} as a {Complexity} {Measure} of {Heart} {Rate} {Variability} ({HRV}) {Signal}},
  volume = {22},
  copyright = {http://creativecommons.org/licenses/by/3.0/},
  url = {https://www.mdpi.com/1099-4300/22/10/1077},
  doi = {10.3390/e22101077},
  language = {en},
  number = {10},
  urldate = {2021-01-12},
  journal = {Entropy},
  author = {Udhayakumar, Radhagayathri and Karmakar, Chandan and Li, Peng and Wang, Xinpei and Palaniswami, Marimuthu},
  month = oct,
  year = {2020},
  pmid = {33286846. PMCID: PMC7597155},
  keywords = {distribution entropy, heart rate variability, complexity analysis, Shannon entropy},
  pages = {1077},
  file = {Full Text PDF:C\:\\Users\\pl806\\Zotero\\storage\\3QJYVV6Z\\Udhayakumar et al. - 2020 - Modified Distribution Entropy as a Complexity Meas.pdf:application/pdf;Snapshot:C\:\\Users\\pl806\\Zotero\\storage\\ZY34N7VZ\\1077.html:text/html}
}

The complexity of a heart rate variability (HRV) signal is considered an important nonlinear feature to detect cardiac abnormalities. This work aims at explaining the physiological meaning of a recently developed complexity measurement method, namely, distribution entropy (DistEn), in the context of HRV signal analysis. We thereby propose modified distribution entropy (mDistEn) to remove the physiological discrepancy involved in the computation of DistEn. The proposed method generates a distance matrix that is devoid of over-exerted multi-lag signal changes. Restricted element selection in the distance matrix makes “mDistEn” a computationally inexpensive and physiologically more relevant complexity measure in comparison to DistEn.

@article{gao_signal_2020,
  title = {Signal {Information} {Prediction} of {Mortality} {Identifies} {Unique} {Patient} {Subsets} after {Severe} {Traumatic} {Brain} {Injury}: {A} {Decision}-{Tree} {Analysis} {Approach}},
  volume = {37},
  issn = {1557-9042},
  shorttitle = {Signal {Information} {Prediction} of {Mortality} {Identifies} {Unique} {Patient} {Subsets} after {Severe} {Traumatic} {Brain} {Injury}},
  doi = {10.1089/neu.2019.6631},
  language = {eng},
  number = {7},
  journal = {Journal of Neurotrauma},
  author = {Gao, Lei and Smielewski, Peter and Li, Peng and Czosnyka, Marek and Ercole, Ari},
  month = apr,
  year = {2020},
  pmid = {31744382. PMCID: PMC7175619},
  keywords = {complexity, decision tree analysis, signal information, TBI},
  pages = {1011--9}
}

Nonlinear physiological signal features that reveal information content and causal flow have recently been shown to be predictors of mortality after severe traumatic brain injury (TBI). The extent to which these features interact together, and with traditional measures to describe patients in a clinically meaningful way remains unclear. In this study, we incorporated basic demographics (age and initial Glasgow Coma Scale [GCS]) with linear and non-linear signal information based features (approximate entropy [ApEn], and multivariate conditional Granger causality [GC]) to evaluate their relative contributions to mortality using cardio-cerebral monitoring data from 171 severe TBI patients admitted to a single neurocritical care center over a 10 year period. Beyond linear modelling, we employed a decision tree analysis approach to define a predictive hierarchy of features. We found ApEn (p = 0.009) and GC (p = 0.004) based features to be independent predictors of mortality at a time when mean intracranial pressure (ICP) was not. Our combined model with both signal information-based features performed the strongest (area under curve = 0.86 vs. 0.77 for linear features only). Although low "intracranial" complexity (ApEn-ICP) outranked both age and GCS as crucial drivers of mortality (fivefold increase in mortality where ApEn-ICP \textless1.56, 36.2% vs. 7.8%), decision tree analysis revealed clear subsets of patient populations using all three predictors. Patients with lower ApEn-ICP who were \textgreater60 years of age died, whereas those with higher ApEn-ICP and GCS ≥5 all survived. Yet, even with low initial intracranial complexity, as long as patients maintained robust GC and "extracranial" complexity (ApEn of mean arterial pressure), they all survived. Incorporating traditional linear and novel, non-linear signal information features, particularly in a framework such as decision trees, may provide better insight into "health" status. However, caution is required when interpreting these results in a clinical setting prior to external validation.

@article{yao_enhanced_2020,
  title = {Enhanced {Automated} {Diagnosis} of {Coronary} {Artery} {Disease} {Using} {Features} {Extracted} {From} {QT} {Interval} {Time} {Series} and {ST}–{T} {Waveform}},
  volume = {8},
  issn = {2169-3536},
  doi = {10.1109/ACCESS.2020.3008965},
  journal = {IEEE Access},
  author = {Yao, Lianke and Liu, Changchun and Li, Peng and Wang, Jikuo and Liu, Yuanyuan and Li, Wang and Wang, Xinpei and Li, Han and Zhang, Huan},
  year = {2020},
  note = {Conference Name: IEEE Access},
  keywords = {electrocardiography, medical signal processing, Time series analysis, cardiovascular system, coronary artery disease, Electrocardiography, Heart rate variability, Arteries, feature extraction, diseases, automated CAD detection system, automated diagnosis, blood vessels, Coronary artery disease (CAD), Diseases, Feature extraction, heart rate variability (HRV), History, learning (artificial intelligence), machine learning (ML), machine learning methods, QT interval time series, QT interval time-series, QT interval variability (QTV), RR interval time-series, signal decomposition, single-lead ECG, ST-T segment abnormalities, ST-T segment waveforms, ST–T waveform, time series},
  pages = {129510--129524},
  file = {IEEE Xplore Abstract Record:C\:\\Users\\pl806\\Zotero\\storage\\SSIVDVQ7\\9139514.html:text/html;IEEE Xplore Full Text PDF:C\:\\Users\\pl806\\Zotero\\storage\\V22L6LLR\\Yao et al. - 2020 - Enhanced Automated Diagnosis of Coronary Artery Di.pdf:application/pdf}
}

There is a growing interest in automated diagnosis of coronary artery disease (CAD) with the application of machine learning (ML) methods to the body surface electrocardiograph (ECG). Although prior studies have documented associations of CAD with increased QT variability and ST-T segment abnormalities such as T-wave inversion and ST-segment elevation or depression, their efficacy in automated CAD detection has not been fully investigated. To validate their usefulness, a dataset containing related clinical characteristics and 5-min single-lead ECGs of 107 healthy controls and 93 CAD patients was first constructed. Based on this dataset, simultaneous analyses were then conducted in five scenarios, in which different ML algorithms were applied to classify the two groups with various features derived from the RR and QT interval time-series and ST-T segment waveforms. Compared with utilizing features obtained from the RR interval time-series, better classification results were achieved utilizing that obtained from the QT interval time-series. The classification results were elevated with combining utilization of features derived from both the RR and QT interval time-series. By further fusing features extracted from ST-T segment waveforms, the best performance was achieved with 96.16% accuracy, 95.75% sensitivity, and 96.40% specificity. Based the best performance, an automated CAD detection system was developed with extreme gradient boosting, an ensemble ML algorithm, and the residual neural network, namely, a deep learning method. The results of this study support the potential of information derived from the QT interval time-series and ST-T segment waveforms in ECG-based automated CAD detection.

@article{wang_short-term_2020,
  title = {Short-{Term} {Heart} {Rate} {Variability} and {Blood} {Biomarkers} of {Gastric} {Cancer} {Prognosis}},
  volume = {8},
  issn = {2169-3536},
  doi = {10.1109/ACCESS.2020.2966378},
  journal = {IEEE Access},
  author = {Wang, Lili and Shi, Bo and Li, Peng and Zhang, Genxuan and Liu, Mulin and Chen, Deli},
  year = {2020},
  keywords = {heart rate variability, Gastric cancer, blood biomarker, cancer prognosis},
  pages = {15159--65},
  file = {IEEE Xplore Abstract Record:C\:\\Users\\pl806\\Zotero\\storage\\QSIT2U2X\\8957432.html:text/html;IEEE Xplore Full Text PDF:C\:\\Users\\pl806\\Zotero\\storage\\VZ97Y53Q\\Wang et al. - 2020 - Short-Term Heart Rate Variability and Blood Biomar.pdf:application/pdf}
}

Inflammation, nutrition, and coagulation play significant roles in cancer prognosis. Autonomic function is also actively involved in tumorigenesis. Previous studies have shown that an elevated C-reactive protein (CRP) level, a serum marker for inflammation, is associated with low heart rate variability (HRV), a common clinical tool for the assessment of autonomic function. It is yet to be investigated whether HRV links to these prognostic factors in cancer patients. Sixty-one patients who were first diagnosed with gastric cancer (GC) were enrolled in this study. Fasting blood samples were collected in the morning seven days before surgery. Blood CRP, prealbumin (PA), and fibrinogen (FIB) were used to assess the inflammation level, nutritional status, and coagulation function respectively. Five-minute resting electrocardiogram (ECG) signals were collected one day before surgical treatment. Short-term HRV time-series were extracted from ECG recordings and were analyzed using commonly-used time- and frequency-domain parameters including standard deviation of normal-to-normal intervals (SDNN), root mean square of successive heartbeat interval differences (RMSSD), very-low-frequency power (VLF), low-frequency power (LF), high-frequency power (HF), total power (TP), LF power in normalized units (LF n.u.), HF power in normalized units (HF n.u.), and ratio of LF to HF (LF/HF). After adjusted for sex, age, body mass index, alcohol consumption, history of diabetes, left ventricular ejection fraction, and hemoglobin levels, our results demonstrated negative associations of HRV with levels of CRP and FIB, while positive associations between HRV and PA level, with effect sizes of as high as 35%–52% standard deviations (SD) changes in CRP, FIB, or PA per 1-SD change in HRV parameters. Therefore, decreased HRV in patients with GC predicts increased burdens of inflammation and coagulation and perturbed nutrition, suggesting that short-term HRV measurement can potentially be a noninvasive biomarker for GC prognosis.

@article{li_more_2019,
  title = {More random motor activity fluctuations predict incident frailty, disability, and mortality},
  volume = {11},
  copyright = {Copyright © 2019 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works. http://www.sciencemag.org/about/science-licenses-journal-article-reuseThis is an article distributed under the terms of the Science Journals Default License.},
  issn = {1946-6234, 1946-6242},
  url = {https://stm.sciencemag.org/content/11/516/eaax1977},
  doi = {10.1126/scitranslmed.aax1977},
  language = {en},
  number = {516},
  urldate = {2019-10-31},
  journal = {Science Translational Medicine},
  author = {Li, Peng and Lim, Andrew S. P. and Gao, Lei and Hu, Chelsea and Yu, Lei and Bennett, David A. and Buchman, Aron S. and Hu, Kun},
  month = oct,
  year = {2019},
  pmid = {31666398. PMCID: PMC7038816},
  pages = {eaax1977},
  file = {Full Text PDF:C\:\\Users\\pl806\\Zotero\\storage\\SDND43MJ\\Li et al. - 2019 - More random motor activity fluctuations predict in.pdf:application/pdf;Snapshot:C\:\\Users\\pl806\\Zotero\\storage\\TS3SXYJG\\eaax1977.html:text/html}
}

Predictive patterns
Fractals are self-similar patterns that exist across different conditions; in medicine, changes in fractal fluctuations can indicate disease, such as degraded fractal movement fluctuations seen with dementia. Using wrist-worn activity monitors, Li et al. analyzed daily motor activity of a large cohort of elderly subjects. They found that more random fluctuations over two time scales (1 to 90 min and greater than 2 hours) predicted increased risk of frailty, disability, and death years later—independent of age, sex, chronic health conditions, and total motor activity. Results suggest that fractal analyses can help predict health outcomes in the absence of overt symptoms and support the utility of passive monitoring.
Mobile healthcare increasingly relies on analytical tools that can extract meaningful information from ambulatory physiological recordings. We tested whether a nonlinear tool of fractal physiology could predict long-term health consequences in a large, elderly cohort. Fractal physiology is an emerging field that aims to study how fractal temporal structures in physiological fluctuations generated by complex physiological networks can provide important information about system adaptability. We assessed fractal temporal correlations in the spontaneous fluctuations of ambulatory motor activity of 1275 older participants at baseline, with a follow-up period of up to 13 years. We found that people with reduced temporal correlations (more random activity fluctuations) at baseline had increased risk of frailty, disability, and all-cause death during follow-up. Specifically, for 1-SD decrease in the temporal activity correlations of this studied cohort, the risk of frailty increased by 31%, the risk of disability increased by 15 to 25%, and the risk of death increased by 26%. These incidences occurred on average 4.7 years (frailty), 3 to 4.2 years (disability), and 5.8 years (death) after baseline. These observations were independent of age, sex, education, chronic health conditions, depressive symptoms, cognition, motor function, and total daily activity. The temporal structures in daily motor activity fluctuations may contain unique prognostic information regarding wellness and health in the elderly population.
More random fluctuations in daily motor activity predict deteriorated quality of life and high death rate in elderly subjects.
More random fluctuations in daily motor activity predict deteriorated quality of life and high death rate in elderly subjects.

@article{yao_comparison_2019,
  title = {Comparison of {QT} interval variability of coronary patients without myocardial infarction with that of patients with old myocardial infarction},
  volume = {113},
  issn = {0010-4825},
  url = {http://www.sciencedirect.com/science/article/pii/S0010482519302732},
  doi = {10.1016/j.compbiomed.2019.103396},
  urldate = {2019-08-28},
  journal = {Computers in Biology and Medicine},
  author = {Yao, Lianke and Li, Peng and Liu, Changchun and Hou, Yunxiu and Yan, Chang and Li, Liping and Li, Ke and Wang, Xinpei and Deogire, Aruna and Du, Chunlei and Zhang, Huan and Wang, Jikuo and Li, Han},
  month = oct,
  year = {2019},
  pmid = {31446319},
  keywords = {Heart rate variability, Myocardial infarction, Coronary artery disease, QT interval variability, Ventricular repolarization},
  pages = {103396},
  file = {ScienceDirect Snapshot:C\:\\Users\\pl806\\Zotero\\storage\\W32VVQZ8\\S0010482519302732.html:text/html}
}

Background
The significant association of myocardial ischemia with elevated QT interval variability (QTV) has been reported in myocardial infarction (MI) patients. However, the influence of the time course of MI on QTV has not been investigated systematically.
Method
Short-term QT and RR interval time series were constructed from the 5 min electrocardiograms of 49 coronary patients without MI and 26 patients with old MI (OMI). The QTV, heart rate variability (HRV), and QT–RR coupling of the two groups were analyzed using various time series analysis tools in the time- and frequency-domains, as well as nonlinear dynamics.
Results
Nearly all of the tested QTV indices for coronary patients with OMI were higher than those for patients without MI. However, no significant differences were found between the two groups in any of the variables employed to assess the HRV and QT–RR coupling. All of the markers that showed statistical significances in univariate analyses still possessed the capabilities of distinguishing between the two groups even after adjusting for studied baseline characteristics, including the coronary atherosclerotic burden.
Conclusions
The results suggested that the QTV increased in coronary patients with OMI compared to those without MI, which might reflect the influence of post-MI remodeling on the beat-to-beat temporal variability of ventricular repolarization. The non-significant differences in the HRV and QT–RR couplings could indicate that there were no differences in the modulation of the autonomic nervous system and interaction of QT with the RR intervals between the two groups.

@article{shi_nonlinear_2019,
  title = {Nonlinear heart rate variability biomarkers for gastric cancer severity: {A} pilot study},
  volume = {9},
  copyright = {2019 The Author(s)},
  issn = {2045-2322},
  shorttitle = {Nonlinear heart rate variability biomarkers for gastric cancer severity},
  url = {https://www.nature.com/articles/s41598-019-50358-y},
  doi = {10.1038/s41598-019-50358-y},
  language = {en},
  number = {1},
  urldate = {2019-10-07},
  journal = {Scientific Reports},
  author = {Shi, Bo and Wang, Lili and Yan, Chang and Chen, Deli and Liu, Mulin and Li, Peng},
  month = sep,
  year = {2019},
  pmid = {31554856. PMCID: PMC6761171},
  pages = {13833},
  file = {Full Text PDF:C\:\\Users\\pl806\\Zotero\\storage\\HUL5T9BL\\Shi et al. - 2019 - Nonlinear heart rate variability biomarkers for ga.pdf:application/pdf;Snapshot:C\:\\Users\\pl806\\Zotero\\storage\\8JKMNYUN\\s41598-019-50358-y.html:text/html}
}

Identifying prognostic factors by affordable tools is crucial for guiding gastric cancer (GC) treatments especially at earlier stages for timing interventions. The autonomic function that is clinically assessed by heart rate variability (HRV) is involved in tumorigenesis. This pilot study was aimed to examine whether nonlinear indices of HRV can be biomarkers of GC severity. Sixty-one newly-diagnosed GC patients were enrolled. Presurgical serum fibrinogen (FIB), carcinoembryonic antigen (CEA), and carbohydrate antigen 19-9 (CA199) were examined. Resting electrocardiogram (ECG) of 5-min was collected prior to surgical treatments to enable the HRV analysis. Twelve nonlinear HRV indices covering the irregularity, complexity, asymmetry, and temporal correlation of heartbeat fluctuations were obtained. Increased short-range temporal correlations, decreased asymmetry, and increased irregularity of heartbeat fluctuations were associated with higher FIB level. Increased irregularity and decreased complexity were also associated with higher CEA level. These associations were independent of age, sex, BMI, alcohol consumption, history of diabetes, left ventricular ejection fraction, and anemia. The results support the hypothesis that perturbations in nonlinear dynamical patterns of HRV predict increased GC severity. Replication in larger samples as well as the examination of longitudinal associations of HRV nonlinear features with cancer prognosis/survival are warranted.

@article{li_dual-input_2019,
  title = {Dual-input {Neural} {Network} {Integrating} {Feature} {Extraction} and {Deep} {Learning} for {Coronary} {Artery} {Disease} {Detection} {Using} {Electrocardiogram} and {Phonocardiogram}},
  volume = {7},
  doi = {10.1109/ACCESS.2019.2943197},
  journal = {IEEE Access},
  author = {Li, H. and Wang, X. and Liu, C. and Wang, Y. and Li, P. and Tang, H. and Yao, L. and Zhang, H.},
  month = sep,
  year = {2019},
  keywords = {coronary artery disease, ECG, Electrocardiography, classification, Arteries, deep learning, Deep learning, feature extraction, PCG, Feature extraction, Phonocardiography, Neural networks, Time-frequency analysis},
  pages = {146457--69},
  file = {IEEE Xplore Abstract Record:C\:\\Users\\pl806\\Zotero\\storage\\8EWBXYTT\\8846698.html:text/html;IEEE Xplore Full Text PDF:C\:\\Users\\pl806\\Zotero\\storage\\2AIF3VN7\\Li et al. - 2019 - Dual-input Neural Network Integrating Feature Extr.pdf:application/pdf}
}

Electrocardiogram (ECG) and phonocardiogram (PCG) signals reflect the electrical and mechanical activities of the heart, respectively. Although studies have documented that some abnormalities in ECG and PCG signals are associated with coronary artery disease (CAD), only few researches have combined the two signals for automatic CAD detection. This paper aims to differentiate between CAD and non-CAD groups using simultaneously collected ECG and PCG signals. To entirely exploit the underlying information in these signals, a novel dual-input neural network that integrates the feature extraction and deep learning methods is developed. First, the ECG and PCG features are extracted from multiple domains, and the information gain ratio is used to select important features. On the other hand, the ECG signal and the decomposed PCG signal (at four scales) are concatenated as a five-channel signal. Then, the selected features and the five-channel signal are fed into the proposed network composed of a fully connected model and a deep learning model. The results show that the classification performance of either feature extraction or deep learning is insufficient when using only ECG or PCG signal, and combining the two signals improves the performance. Further, when using the proposed network, the best result is obtained with accuracy, sensitivity, specificity, and G-mean of 95.62%, 98.48%, 89.17%, and 93.69%, respectively. Comparisons with existing studies demonstrate that the proposed network can effectively capture the combined information of ECG and PCG signals for the recognition of CAD.

@article{li_interaction_2019,
  title = {Interaction between the progression of {Alzheimer}'s disease and fractal degradation},
  volume = {83},
  issn = {1558-1497},
  doi = {10.1016/j.neurobiolaging.2019.08.023},
  language = {eng},
  journal = {Neurobiology of Aging},
  author = {Li, Peng and Yu, Lei and Yang, Jingyun and Lo, Men-Tzung and Hu, Chelsea and Buchman, Aron S. and Bennett, David A. and Hu, Kun},
  month = aug,
  year = {2019},
  pmid = {31585364. PMCID: PMC6858962},
  keywords = {Cognitive impairment, Fractal regulation, Longitudinal study, Motor activity, Pathological aging},
  pages = {21--30},
  file = {Full Text:C\:\\Users\\pl806\\Zotero\\storage\\9J73B6YP\\Li et al. - 2019 - Interaction between the progression of Alzheimer's.pdf:application/pdf}
}

Many outputs from healthy neurophysiological systems including motor activity display nonrandom fluctuations with fractal scaling behavior as characterized by similar temporal fluctuation patterns across a range of time scales. Degraded fractal regulation predicts adverse consequences including Alzheimer’s dementia. We examined longitudinal changes in the scaling behavior of motor activity fluctuations during the progression of Alzheimer’s disease (AD) in 1068 participants in the Rush Memory and Aging Project. Motor activity of up to 10 days was recorded annually for up to 13 years. Cognitive assessments and clinical diagnoses were administered annually in the same participants. We found that fractal regulation gradually degraded over time (p \textless 0.0001) even during the stage with no cognitive impairment. The degradation rate was more than doubled after the diagnosis of mild cognitive impairment and more than doubled further after the diagnosis of Alzheimer’s dementia (p’s ≤ 0.0005). Besides, the longitudinal degradation of fractal regulation significantly correlated with the decline in cognitive performance throughout the progression from no cognitive impairment to mild cognitive impairment, and to AD (p \textless 0.001). All effects remained the same in subsequent sensitivity analyses that included only 255 decedents with autopsy-confirmed Alzheimer’s pathology. These results indicate that the progression of AD accelerates fractal degradation and that fractal degradation may be an integral part of the process of AD.

@article{yan_novel_2019,
  title = {Novel gridded descriptors of poincaré plot for analyzing heartbeat interval time-series},
  volume = {109},
  issn = {0010-4825},
  url = {http://www.sciencedirect.com/science/article/pii/S0010482519301246},
  doi = {10.1016/j.compbiomed.2019.04.015},
  urldate = {2019-05-30},
  journal = {Computers in Biology and Medicine},
  author = {Yan, Chang and Li, Peng and Liu, Changchun and Wang, Xinpei and Yin, Chunyan and Yao, Lianke},
  month = jun,
  year = {2019},
  pmid = {31100581},
  keywords = {Coronary artery disease, Poincaré plot, Gridded distribution entropy, Gridded distribution rate, Short-term time-series},
  pages = {280--9},
  file = {ScienceDirect Snapshot:C\:\\Users\\pl806\\Zotero\\storage\\Q6BA8Q34\\S0010482519301246.html:text/html}
}

A Poincaré plot is a return map that geometrically elucidates the progression of a time-series. It has frequently been used in heart rate variability analyses. However, algorithms for dedicatedly dissecting the shape of this geometrical plot are yet to be established. In this study, we proposed a gridded Poincaré plot by coarse-graining the original graph and using the newly proposed one, defined two novel measures, namely gridded distribution rate (GDR) and gridded distribution entropy (GDE). The GDR essentially represents the percentage of grids with points, while the GDE estimates the Shannon entropy of the grid weight; that is, the number of points in each grid. The performances of the two measures were examined using both theoretical data with known dynamics and experimental short-term RR interval time-series, and they were compared with several existing metrics. Simulation tests demonstrated that both the GDR and GDE could distinguish among different dynamics, while all the compared methods failed. The experimental results further indicated the ability of the GDR and GDE to differentiate healthy young people from healthy aged adults as well as distinguish healthy subjects from patients with coronary artery disease. Our results suggest that the proposed GDR and GDE may better characterize the Poincaré plot in terms of differentiating between varying dynamical regimes, and between human physiological or pathological conditions. Further studies are warranted to establish their feasibility in evaluating cardiovascular functions in clinical practice.

@article{gao_nocturnal_2019,
  title = {Nocturnal {Heart} {Rate} {Variability} {Moderates} the {Association} {Between} {Sleep}–{Wake} {Regularity} and {Mood} in {Young} {Adults}},
  volume = {42},
  issn = {0161-8105},
  url = {https://academic.oup.com/sleep/article/42/5/zsz034/5307029},
  doi = {10.1093/sleep/zsz034},
  language = {en},
  number = {5},
  urldate = {2019-05-30},
  journal = {Sleep},
  author = {Gao, Lei and Li, Peng and Hu, Chelsea and To, Tommy and Patxot, Melissa and Falvey, Brigid and Wong, Patricia M. and Scheer, Frank A. J. L. and Lin, Chen and Lo, Men-Tzung and Hu, Kun},
  month = may,
  year = {2019},
  pmid = {30722058. PMCID: PMC6519914},
  pages = {zsz034},
  file = {Full Text PDF:C\:\\Users\\pl806\\Zotero\\storage\\U8WGWLKM\\Gao et al. - 2019 - Nocturnal heart rate variability moderates the ass.pdf:application/pdf;Snapshot:C\:\\Users\\pl806\\Zotero\\storage\\QZFI6687\\5307029.html:text/html}
}

AbstractStudy Objectives.  Sleep–wake regularity (SWR) is often disrupted in college students and mood disorders are rife at this age. Disrupted SWR can cause r

@article{yan_impacts_2019,
  title = {Impacts of reference points and reference lines on the slope- and area-based heart rate asymmetry analysis},
  volume = {137},
  issn = {0263-2241},
  url = {http://www.sciencedirect.com/science/article/pii/S0263224119300715},
  doi = {10.1016/j.measurement.2019.01.062},
  urldate = {2019-03-27},
  journal = {Measurement},
  author = {Yan, Chang and Li, Peng and Yao, Lianke and Karmakar, Chandan and Liu, Changchun},
  month = apr,
  year = {2019},
  keywords = {Area index, Heart rate asymmetry (HRA), Poincaré plot, Slope index},
  pages = {515--26},
  file = {ScienceDirect Snapshot:C\:\\Users\\pl806\\Zotero\\storage\\SPALCU9J\\S0263224119300715.html:text/html}
}

Heart rate asymmetry (HRA) could capture valuable dynamical properties from the electrocardiographic RR interval time-series that are helpful for evaluating the cardiovascular functioning. Several metrics derived from the Poincaré plot have been established for assessing HRA such as the slope index (SI) and the area index (AI). In the current study, we aimed to examine how different reference points and reference lines affect the calculations of SI and AI. To understand their performance, two case studies that were to classify subjects with (1) arrhythmias and (2) congestive heart failure, respectively, from normal controls were performed. To examine whether these effects depend on data lengths, the case studies were performed on both long-term heartbeat interval time-series and short-term segments. Our results showed that different reference points or reference lines could strongly affect the performance of both SI and AI, especially when short-term data were being analyzed. Using the minimum of data as the reference point might be a conservative solution in application but a spectrum of SI or AI measurements with multiple reference points and reference lines are highly recommended.

@article{li_ez_2019,
  title = {{EZ} {Entropy}: a software application for the entropy analysis of physiological time-series},
  volume = {18},
  issn = {1475-925X},
  shorttitle = {{EZ} {Entropy}},
  url = {https://doi.org/10.1186/s12938-019-0650-5},
  doi = {10.1186/s12938-019-0650-5},
  number = {1},
  urldate = {2019-03-27},
  journal = {BioMedical Engineering OnLine},
  author = {Li, Peng},
  month = mar,
  year = {2019},
  pmid = {30894180. PMCID: PMC6425722},
  pages = {30},
  file = {Full Text PDF:C\:\\Users\\pl806\\Zotero\\storage\\3ALKND59\\Li - 2019 - EZ Entropy a software application for the entropy.pdf:application/pdf;Snapshot:C\:\\Users\\pl806\\Zotero\\storage\\CFNSZ5Z2\\s12938-019-0650-5.html:text/html}
}

Entropy analysis has been attracting increasing attentions in the recent two or three decades. It assesses complexity, or irregularity, of time-series which is extraordinarily relevant to physiology and diseases as demonstrated by tremendous studies. However, the complexity can hardly be appreciated by traditional methods including time-, frequency-domain analysis, and time-frequency analysis that are the common built-in options in commercialized measurement and statistical software. To facilitate the entropy analysis of physiological time-series, a new software application, namely EZ Entropy, was developed and introduced in this article.

@article{li_short-term_2019,
  title = {Short-term {QT} interval variability in patients with coronary artery disease and congestive heart failure: a comparison with healthy control subjects},
  volume = {57},
  issn = {1741-0444},
  shorttitle = {Short-term {QT} interval variability in patients with coronary artery disease and congestive heart failure},
  url = {https://doi.org/10.1007/s11517-018-1870-8},
  doi = {10.1007/s11517-018-1870-8},
  language = {en},
  number = {2},
  urldate = {2019-03-27},
  journal = {Medical \& Biological Engineering \& Computing},
  author = {Li, Yang and Li, Peng and Wang, Xinpei and Karmakar, Chandan and Liu, Changchun and Liu, Chengyu},
  month = feb,
  year = {2019},
  pmid = {30143993},
  keywords = {Sample entropy, QT interval variability, Dynamical patterns, Permutation entropy, QT variability index},
  pages = {389--400}
}

This study aimed to test how different QT interval variability (QTV) indices change in patients with coronary artery disease (CAD) and congestive heart failure (CHF). Twenty-nine healthy volunteers, 29 age-matched CAD patients, and 20 age-matched CHF patients were studied. QT time series were derived from 5-min resting lead-II electrocardiogram (ECG). Time domain indices [mean, SD, and QT variability index (QTVI)], frequency-domain indices (LF and HF), and nonlinear indices [sample entropy (SampEn), permutation entropy (PE), and dynamical patterns] were calculated. In order to account for possible influence of heart rate (HR) on QTV, all the calculations except QTVI were repeated on HR-corrected QT time series (QTc) using three correction methods (i.e., Bazett, Fridericia, and Framingham method). Results showed that CHF patients exhibited increased mean, increased SD, increased LF and HF, decreased T-wave amplitude, increased QTVI, and decreased PE, while showed no significant changes in SampEn. Interestingly, CHF patients also showed significantly changed distribution of the dynamical patterns with less monotonously changing patterns while more fluctuated patterns. In CAD group, only QTVI was found significantly increased as compared with healthy controls. Results after HR correction were in common with those before HR correction except for QTc based on Bazett correction. Open image in new window Graphical abstract Fig. The framework of this paper. The arrows show the sequential analysis of the data.

@article{li_variability_2019,
  title = {Variability of {Cardiac} {Electromechanical} {Delay} {With} {Application} to the {Noninvasive} {Detection} of {Coronary} {Artery} {Disease}},
  volume = {7},
  issn = {2169-3536},
  doi = {10.1109/ACCESS.2019.2911555},
  journal = {IEEE Access},
  author = {Li, Y. and Wang, X. and Liu, C. and Li, L. and Yan, C. and Yao, L. and Li, P.},
  year = {2019},
  keywords = {electrocardiogram, electrocardiography, entropy, heart rate variability, medical signal processing, signal classification, sample entropy, coronary artery disease, permutation entropy, Electrocardiography, Heart rate variability, HRV, classification, support vector machine, Arteries, frequency-domain analysis, diseases, blood vessels, Feature extraction, heart rate variability (HRV), CAD patients, cardiac electromechanical delay, cardiac function assessment, coronary artery disease (CAD), diastolic period variability, diastolic period variability (DPV), dynamical pattern analysis, dynamical patterns, Electromechanical delay (EMD), EMD variability, EMDV analysis, frequency-domain measures, mechanical activities, myocardial electrical activities, noninvasive detection, phonocardiogram signals, phonocardiography, Phonocardiography, support vector machines, Support vector machines, systolic period variability (SPV), time-domain analysis, time-domain measures},
  pages = {53115--24},
  file = {IEEE Xplore Abstract Record:C\:\\Users\\pl806\\Zotero\\storage\\TU3684HP\\8692727.html:text/html;IEEE Xplore Full Text PDF:C\:\\Users\\pl806\\Zotero\\storage\\PN75IBBJ\\Li et al. - 2019 - Variability of Cardiac Electromechanical Delay Wit.pdf:application/pdf}
}

Heart rate variability (HRV), systolic period variability (SPV), and diastolic period variability (DPV) have shown potential for assessing cardiac function. It is unknown whether the time delay between the myocardial electrical and mechanical activities (i.e., electromechanical delay, EMD) also possesses variability, and if it does, whether this EMD variability (EMDV) could render additional value for cardiac function assessment. In this paper, we extracted the beat-to-beat EMD from 5-min simultaneously recorded electrocardiogram and phonocardiogram signals in 30 patients with coronary artery disease (CAD) and 30 healthy control subjects, and studied its variability using the same methods as applied for HRV including time-domain measures [mean and standard deviation (SD)], frequency-domain measures [normalized low- and high-frequency (LFn, HFn) and LF/HF)], and nonlinear measures [sample entropy (SampEn), permutation entropy (PE), and dynamical patterns]. In addition, we examined whether the addition of EMDV could offer improved performance for distinguishing between the two groups compared to using the HRV, SPV, and DPV features. Support vector machine with 10-fold cross-validation was used for classification. Results showed increased SD of SPV, increased mean, SD and decreased SampEn of EMDV in CAD patients. Besides, the dynamical pattern analysis showed that CAD patients had significantly increased fluctuated patterns and decreased monotonous patterns in EMDV. In particular, the addition of EMDV indices dramatically increased the classification accuracy from 0.729 based on HRV, SPV, and DPV features to 0.958. Our results suggest promising of the EMDV analysis that could potentially be helpful for detecting CAD noninvasively.

@article{azami_fuzzy_2019,
  title = {Fuzzy {Entropy} {Metrics} for the {Analysis} of {Biomedical} {Signals}: {Assessment} and {Comparison}},
  volume = {7},
  issn = {2169-3536},
  shorttitle = {Fuzzy {Entropy} {Metrics} for the {Analysis} of {Biomedical} {Signals}},
  doi = {10.1109/ACCESS.2019.2930625},
  journal = {IEEE Access},
  author = {Azami, H. and Li, P. and Arnold, S. E. and Escudero, J. and Humeau-Heurtier, A.},
  year = {2019},
  keywords = {entropy, medical signal processing, fuzzy set theory, sample entropy, Electroencephalography, Entropy, Electromyography, Muscles, biomedical signals, centre of gravity, defuzzification, exponential MF, FuzEn-based methods, Fuzzy entropy, fuzzy entropy metrics, fuzzy membership functions, Fuzzy sets, irregularity, long signals, physiological signals, short real signals, short signals, undefined entropy values, Z-shaped MF},
  pages = {104833--47},
  file = {IEEE Xplore Abstract Record:C\:\\Users\\pl806\\Zotero\\storage\\WQT32SPV\\8769842.html:text/html;IEEE Xplore Full Text PDF:C\:\\Users\\pl806\\Zotero\\storage\\ZLIB62LK\\Azami et al. - 2019 - Fuzzy Entropy Metrics for the Analysis of Biomedic.pdf:application/pdf}
}

Fuzzy entropy (FuzEn) was introduced to alleviate limitations associated with sample entropy (SampEn) in the analysis of physiological signals. Over the past decade, FuzEn-based methods have been widely used in various real-world biomedical applications. Several fuzzy membership functions (MFs), including triangular, trapezoidal, Z-shaped, bell-shaped, Gaussian, constant-Gaussian, and exponential functions have been employed in FuzEn. However, these FuzEn-based metrics have not been systematically compared yet. Since the threshold value r used in FuzEn is not directly comparable across different MFs, we here propose to apply a defuzzification approach using a surrogate parameter called ’center of gravity’ to re-enable a fair and direct comparison. To evaluate these MFs, we analyze several synthetic and three clinical datasets. FuzEn using the triangular, trapezoidal, and Z-shaped MFs may lead to undefined entropy values for short signals, thus providing a very limited advantage over SampEn. When dealing with an equal value of the center of gravity, the Gaussian MF, as the fastest algorithm, results in the highest Hedges’ g effect size for long signals. Our results also indicate that the FuzEn based on exponential MF of order four better distinguishes short white, pink, and brown noises, and yields more significant differences for the short real signals based on Hedges’ g effect size. The triangular, trapezoidal, and Z-shaped MFs are not recommended for short signals. We propose to use FuzEn with Gaussian and exponential MF of order four for characterization of short (around 50-400 sample points) and long data (longer than 500 sample points), respectively. We expect FuzEn with Gaussian and exponential MF as well as the concept of defuzzification to play prominent roles in the irregularity analysis of biomedical signals. The MATLAB codes for the FuzEn with different MFs are available at https://github.com/HamedAzami/FuzzyEntropy_Matlab.

@article{wu_circadian_2018,
  title = {Circadian {Rest}-{Activity} {Rhythms} {Predict} {Cognitive} {Function} in {Early} {Parkinson}'s {Disease} {Independently} of {Sleep}},
  volume = {5},
  issn = {2330-1619},
  doi = {10.1002/mdc3.12692},
  language = {eng},
  number = {6},
  journal = {Movement Disorders Clinical Practice},
  author = {Wu, Jade Q. and Li, Peng and Stavitsky Gilbert, Karina and Hu, Kun and Cronin-Golomb, Alice},
  month = dec,
  year = {2018},
  pmid = {30637282. PMCID: PMC6277371},
  keywords = {sleep, cognition, Parkinson's disease, circadian rest-activity rhythm, circadian rest‐activity rhythm, nonmotor symptoms},
  pages = {614--9},
  file = {Snapshot:C\:\\Users\\pl806\\Zotero\\storage\\XQNELJST\\mdc3.html:text/html}
}

Background: Cognitive impairment is a common and debilitating symptom of Parkinson’s disease (PD), and its etiology is likely multifactorial. One candidate mechanism is circadian disruption. Although there is evidence of circadian abnormalities in PD, no studies have directly assessed their association with cognitive impairment.
Objectives: Investigate whether circadian rest-activity rhythm is associated with cognitive function in PD independently of sleep.
Methods: Thirty-five participants with PD wore wrist actigraph monitors and completed sleep diaries for 7 to 10 days, then underwent neuropsychological testing. Rest-activity rhythm was characterized using nonparametric circadian rhythm analysis of actigraphy data. Objective sleep parameters were also estimated using actigraphy data. Hierarchical regression models assessed the independent contributions of sleep and rest-activity rhythm to cognitive performance.
Results: Less stable day-to-day rest-activity rhythm was associated with poorer executive, visuospatial, and psychomotor functioning, but not with memory. Hierarchical regressions showed that interdaily stability’s contribution to cognitive performance was independent of sleep’s contributions. Whereas sleep contributed to executive function, but not psychomotor or visuospatial performance, rest-activity rhythm stability significantly contributed to variance in all three of these domains, uniquely accounting for 14.4% to 17.6% of their performance variance.
Conclusions: Our findings indicate that circadian rest-activity rhythm is associated with cognitive impairment independently of sleep. This suggests the possible utility of rest-activity rhythm as a biomarker for circadian function in PD. Future research should explore interventions to stabilize behavioral rhythms in order to strengthen circadian function, which, in turn, may reduce cognitive impairment in PD.

@article{li_fractal_2018,
  title = {Fractal regulation and incident {Alzheimer}'s disease in elderly individuals},
  volume = {14},
  issn = {1552-5260},
  url = {http://www.sciencedirect.com/science/article/pii/S1552526018301080},
  doi = {10.1016/j.jalz.2018.03.010},
  number = {9},
  urldate = {2018-09-10},
  journal = {Alzheimer's \& Dementia},
  author = {Li, Peng and Yu, Lei and Lim, Andrew S. P. and Buchman, Aron S. and Scheer, Frank A. J. L. and Shea, Steven A. and Schneider, Julie A. and Bennett, David A. and Hu, Kun},
  month = sep,
  year = {2018},
  pmid = {29733807. PMCID: PMC6201319},
  keywords = {Mild cognitive impairment, Fractal physiology, Fractal regulation, Longitudinal cohort study, Prediction of Alzheimer's disease},
  pages = {1114--25},
  file = {ScienceDirect Full Text PDF:C\:\\Users\\pl806\\Zotero\\storage\\Y2NU3MML\\Li et al. - 2018 - Fractal regulation and incident Alzheimer's diseas.pdf:application/pdf;ScienceDirect Snapshot:C\:\\Users\\pl806\\Zotero\\storage\\IHLDAFD4\\S1552526018301080.html:text/html}
}

Introduction
Healthy physiological systems exhibit fractal regulation (FR), generating similar fluctuation patterns in physiological outputs across different time scales. FR in motor activity is degraded in dementia, and the degradation correlates to cognitive decline. We tested whether degraded FR predicts Alzheimer’s dementia.
Methods
FR in motor activity was assessed in 1097 nondemented older adults at baseline. Cognition was assessed annually for up to 11 years.
Results
Participants with an FR metric at the 10th percentile in this cohort had a 1.8-fold Alzheimer’s disease risk (equivalent to the effect of being ∼5.2 years older) and 1.3-fold risk for mild cognitive impairment (equivalent to the effect of being ∼3.0 years older) than those at the 90th percentile. Consistently, degraded FR predicted faster cognitive decline. These associations were independent of physical activity, sleep fragmentation, and stability of daily activity rhythms.
Discussion
FR may be a useful tool for predicting Alzheimer’s dementia.

@article{jiang_modeling_2018,
  title = {Modeling radial artery pressure waveforms using curve fitting: {Comparison} of four types of fitting functions},
  volume = {23},
  issn = {1872-9312},
  shorttitle = {Modeling radial artery pressure waveforms using curve fitting},
  url = {http://www.sciencedirect.com/science/article/pii/S1872931218300632},
  doi = {10.1016/j.artres.2018.08.003},
  urldate = {2018-09-10},
  journal = {Artery Research},
  author = {Jiang, Xinge and Wei, Shoushui and Ji, Jingbo and Liu, Feifei and Li, Peng and Liu, Chengyu},
  month = sep,
  year = {2018},
  keywords = {Curve fitting, Double-exponential function, Gaussian function, Logarithmic normal function, Mean absolute error, Radial artery pressure waveform (RAPW), Raleigh function},
  pages = {56--62},
  file = {ScienceDirect Snapshot:C\:\\Users\\pl806\\Zotero\\storage\\J78T5XIW\\S1872931218300632.html:text/html}
}

Background
Curve fitting has been intensively used to model artery pressure waveform (APW). The modelling accuracy can greatly influence the calculation of APWs parameters that serve as quantitative measures for assessing the morphological characteristics of APWs. However, it is unclear which fitting function is more suitable for APW. In this paper, we compared the fitting accuracies of four types of fitting functions, including Raleigh function, double-exponential function, Gaussian function, and logarithmic normal function, in modeling radial artery pressure waveform (RAPW).
Methods
RAPWs were recorded from 24 healthy subjects in resting supine position. To perform curve fitting, 10 consecutive stable RAPWs for each subject were randomly selected and each waveform was fitted using three instances of the same fitting function.
Results
The mean absolute percentage errors (MAPE) of the fitting results were 5.89% ± 0.46% (standard deviation), 3.31% ± 0.22%, 2.25% ± 0.31%, and 1.49% ± 0.28% for Raleigh function, double-exponential function, Gaussian function, and logarithmic normal function, respectively. Their corresponding mean maximum residual errors were 23.71%, 17.83%, 6.11%, and 5.49%.
Conclusions
The performance of using Gaussian function and logarithmic normal function to model RAPW is comparable, and is better than that of using Raleigh function and double-exponential function.

@article{wang_does_2018,
  title = {Does the {Temporal} {Asymmetry} of {Short}-{Term} {Heart} {Rate} {Variability} {Change} during {Regular} {Walking}? {A} {Pilot} {Study} of {Healthy} {Young} {Subjects}},
  volume = {2018},
  issn = {1748-670X, 1748-6718},
  shorttitle = {Does the {Temporal} {Asymmetry} of {Short}-{Term} {Heart} {Rate} {Variability} {Change} during {Regular} {Walking}?},
  url = {https://www.hindawi.com/journals/cmmm/2018/3543048/},
  doi = {10.1155/2018/3543048},
  language = {en},
  journal = {Computational and Mathematical Methods in Medicine},
  author = {Wang, Xinpei and Yan, Chang and Shi, Bo and Liu, Changchun and Karmakar, Chandan and Li, Peng},
  month = apr,
  year = {2018},
  pmid = {29853984 PMCID: PMC5952585},
  pages = {3543048}
}

@article{li_detection_2018,
  title = {Detection of epileptic seizure based on entropy analysis of short-term {EEG}},
  volume = {13},
  issn = {1932-6203},
  url = {http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0193691},
  doi = {10.1371/journal.pone.0193691},
  language = {en},
  number = {3},
  urldate = {2018-05-04},
  journal = {PLOS ONE},
  author = {Li, Peng and Karmakar, Chandan and Yearwood, John and Venkatesh, Svetha and Palaniswami, Marimuthu and Liu, Changchun},
  month = mar,
  year = {2018},
  pmid = {29543825. PMCID: PMC5854404},
  keywords = {nonlinear dynamics, Electroencephalography, Algorithms, Entropy, Epilepsy, Bandpass filters, Eyes, Statistical data},
  pages = {e0193691},
  file = {Full Text PDF:C\:\\Users\\pl806\\Zotero\\storage\\D4DZQZG8\\Li 等. - 2018 - Detection of epileptic seizure based on entropy an.pdf:application/pdf;Snapshot:C\:\\Users\\pl806\\Zotero\\storage\\KSQTKCU7\\article.html:text/html}
}

Entropy measures that assess signals’ complexity have drawn increasing attention recently in biomedical field, as they have shown the ability of capturing unique features that are intrinsic and physiologically meaningful. In this study, we applied entropy analysis to electroencephalogram (EEG) data to examine its performance in epilepsy detection based on short-term EEG, aiming at establishing a short-term analysis protocol with optimal seizure detection performance. Two classification problems were considered, i.e., 1) classifying interictal and ictal EEGs (epileptic group) from normal EEGs; and 2) classifying ictal from interictal EEGs. For each problem, we explored two protocols to analyze the entropy of EEG: i) using a single analytical window with different window lengths, and ii) using an average of multiple windows for each window length. Two entropy methods—fuzzy entropy (FuzzyEn) and distribution entropy (DistEn)–were used that have valid outputs for any given data lengths. We performed feature selection and trained classifiers based on a cross-validation process. The results show that performance of FuzzyEn and DistEn may complement each other and the best performance can be achieved by combining: 1) FuzzyEn of one 5-s window and the averaged DistEn of five 1-s windows for classifying normal from epileptic group (accuracy: 0.93, sensitivity: 0.91, specificity: 0.96); and 2) the averaged FuzzyEn of five 1-s windows and DistEn of one 5-s window for classifying ictal from interictal EEGs (accuracy: 0.91, sensitivity: 0.93, specificity: 0.90). Further studies are warranted to examine whether this proposed short-term analysis procedure can help track the epileptic activities in real time and provide prompt feedback for clinical practices.

@article{ji_increased_2018,
  title = {Increased pulse wave transit time after percutaneous coronary intervention procedure in {CAD} patients},
  volume = {8},
  issn = {2045-2322},
  doi = {10.1038/s41598-017-18520-6},
  language = {eng},
  number = {1},
  journal = {Scientific Reports},
  author = {Ji, Lizhen and Liu, Chengyu and Li, Peng and Wang, Xinpei and Liu, Changchun and Hou, Yinglong},
  month = jan,
  year = {2018},
  pmid = {29311630. PMCID: PMC5758522},
  pages = {115}
}

Pulse wave transit time (PWTT) has been widely used as an index in assessing arterial stiffness. Percutaneous coronary intervention (PCI) is usually applied to the treatment of coronary artery disease (CAD). Research on the changes in PWTT caused by PCI is helpful for understanding the impact of the PCI procedure. In addition, effects of stent sites and access sites on the changes in PWTT have not been explored. Consequently, this study aimed to provide this information. The results showed that PWTT significantly increased after PCI (p \textless 0.01) while the standard deviation (SD) of PWTT time series had no statistically significant changes (p = 0.60) between before and after PCI. Significantly increased PWTT was found in the radial access group (p \textless 0.01), while there were no significant changes in the femoral access group (p \textgreater 0.4). Additionally, PWTT in the left anterior descending (LAD) group significantly increased after PCI (p \textless 0.01), but the increase that was found in the right coronary artery (RCA) group was not significant (p \textgreater 0.1). Our study indicates that arterial elasticity and left ventricular functions can benefit from a successful PCI procedure, and the increase of peripheral PWTT after PCI can help to better understand the effectiveness of the procedure.

@article{zheng_advanced_2018,
  title = {Advanced {Signal} {Processing} for {Cardiovascular} and {Neurological} {Diseases}},
  volume = {2018},
  issn = {1748-6718},
  doi = {10.1155/2018/3416540},
  language = {eng},
  journal = {Computational and Mathematical Methods in Medicine},
  author = {Zheng, Dingchang and Chen, Fei and Li, Peng and Peng, Sheng-Yu},
  year = {2018},
  pmid = {30073030. PMCID: PMC6057415},
  keywords = {Humans, Cardiovascular Diseases, Signal Processing, Computer-Assisted, Diagnosis, Computer-Assisted, Nervous System Diseases},
  pages = {3416540},
  file = {Full Text:C\:\\Users\\pl806\\Zotero\\storage\\VB6X7ZGL\\Zheng et al. - 2018 - Advanced Signal Processing for Cardiovascular and .pdf:application/pdf}
}

@article{yan_area_2017,
  title = {Area asymmetry of heart rate variability signal},
  volume = {16},
  issn = {1475-925X},
  doi = {10.1186/s12938-017-0402-3},
  language = {eng},
  journal = {Biomedical Engineering Online},
  author = {Yan, C. and Li, P. and Ji, L. and Yao, L. and Karmakar, C. and Liu, C.},
  month = sep,
  year = {2017},
  pmcid = {PMC5607847},
  pages = {112},
  annote = {The following values have no corresponding Zotero field:abbr-2: Biomed. Eng. Onlinenumber: 1edition: 2017/09/21accession-num: 28934961}
}

Heart rate fluctuates beat-by-beat asymmetrically which is known as heart rate asymmetry (HRA). It is challenging to assess HRA robustly based on short-term heartbeat interval series.\textbarAn area index (AI) was developed that combines the distance and phase angle information of points in the Poincaré plot. To test its performance, the AI was used to classify subjects with: (i) arrhythmia, and (ii) congestive heart failure, from the corresponding healthy controls. For comparison, the existing Porta’s index (PI), Guzik’s index (GI), and slope index (SI) were calculated. To test the effect of data length, we performed the analyses separately using long-term heartbeat interval series (derived from \textgreater3.6-h ECG) and short-term segments (with length of 500 intervals). A second short-term analysis was further carried out on series extracted from 5-min ECG.\textbarFor long-term data, SI showed acceptable performance for both tasks, i.e., for task i p \textless 0.001, Cohen’s d = 0.93, AUC (area under the receiver-operating characteristic curve) = 0.86; for task ii p \textless 0.001, d = 0.88, AUC = 0.75. AI performed well for task ii (p \textless 0.001, d = 1.0, AUC = 0.78); for task i, though the difference was statistically significant (p \textless 0.001, AUC = 0.76), the effect size was small (d = 0.11). PI and GI failed in both tasks (p \textgreater 0.05, d \textless 0.4, AUC \textless 0.7 for all). However, for short-term segments, AI indicated better distinguishability for both tasks, i.e., for task i, p \textless 0.001, d = 0.71, AUC = 0.71; for task ii, p \textless 0.001, d = 0.93, AUC = 0.74. The rest three measures all failed with small effect sizes and AUC values (d \textless 0.5, AUC \textless 0.7 for all) although the difference in SI for task i was statistically significant (p \textless 0.001). Besides, AI displayed smaller variations across different short-term segments, indicating more robust performance. Results from the second short-term analysis were in keeping with those findings.\textbarThe proposed AI indicated better performance especially for short-term heartbeat interval data, suggesting potential in the ambulatory application of cardiovascular monitoring.

@article{li_reduced_2017,
  title = {Reduced {Tolerance} to {Night} {Shift} in {Chronic} {Shift} {Workers}: {Insight} {From} {Fractal} {Regulation}},
  volume = {40},
  issn = {0161-8105, 1550-9109},
  shorttitle = {Reduced {Tolerance} to {Night} {Shift} in {Chronic} {Shift} {Workers}},
  url = {https://academic.oup.com/sleep/article-lookup/doi/10.1093/sleep/zsx092},
  doi = {10.1093/sleep/zsx092},
  language = {en},
  number = {7},
  urldate = {2017-07-18},
  journal = {Sleep},
  author = {Li, Peng and Morris, Christopher J. and Patxot, Melissa and Yugay, Tatiana and Mistretta, Joseph and Purvis, Taylor E. and Scheer, Frank A. J. L. and Hu, Kun},
  month = jul,
  year = {2017},
  pmid = {28838129. PMCID: PMC6317507},
  pages = {zsx092}
}

@article{li_fractal_2017,
  title = {Fractal regulation in temporal activity fluctuations: {A} biomarker for circadian control and beyond},
  volume = {3},
  number = {1},
  journal = {JSM Biomarkers},
  author = {Li, Peng and To, Tommy and Chiang, Wei-Yin and Escobar, Carolina and Buijs, Ruud M. and Hu, Kun},
  year = {2017},
  pmid = {28553673. PMCID: PMC5443249},
  pages = {1008}
}

@article{karmakar_stability_2017,
  title = {Stability, {Consistency} and {Performance} of {Distribution} {Entropy} in {Analysing} {Short} {Length} {Heart} {Rate} {Variability} ({HRV}) {Signal}},
  volume = {8},
  issn = {1664-042X},
  doi = {10.3389/fphys.2017.00720},
  language = {eng},
  journal = {Frontiers in Physiology},
  author = {Karmakar, Chandan and Udhayakumar, Radhagayathri K. and Li, Peng and Venkatesh, Svetha and Palaniswami, Marimuthu},
  year = {2017},
  pmid = {28979215. PMCID: PMC5611446},
  keywords = {distribution entropy, heart rate variability, sample entropy, approximate entropy, aging, arrhythmia, short-term analysis},
  pages = {720},
  file = {Full Text:C\:\\Users\\pl806\\Zotero\\storage\\8WFZ59A5\\Karmakar et al. - 2017 - Stability, Consistency and Performance of Distribu.pdf:application/pdf}
}

Distribution entropy (DistEn) is a recently developed measure of complexity that is used to analyse heart rate variability (HRV) data. Its calculation requires two input parameters-the embedding dimension m, and the number of bins M which replaces the tolerance parameter r that is used by the existing approximation entropy (ApEn) and sample entropy (SampEn) measures. The performance of DistEn can also be affected by the data length N. In our previous studies, we have analyzed stability and performance of DistEn with respect to one parameter (m or M) or combination of two parameters (N and M). However, impact of varying all the three input parameters on DistEn is not yet studied. Since DistEn is predominantly aimed at analysing short length heart rate variability (HRV) signal, it is important to comprehensively study the stability, consistency and performance of the measure using multiple case studies. In this study, we examined the impact of changing input parameters on DistEn for synthetic and physiological signals. We also compared the variations of DistEn and performance in distinguishing physiological (Elderly from Young) and pathological (Healthy from Arrhythmia) conditions with ApEn and SampEn. The results showed that DistEn values are minimally affected by the variations of input parameters compared to ApEn and SampEn. DistEn also showed the most consistent and the best performance in differentiating physiological and pathological conditions with various of input parameters among reported complexity measures. In conclusion, DistEn is found to be the best measure for analysing short length HRV time series.

@article{shi_entropy_2017,
  title = {Entropy {Analysis} of {Short}-{Term} {Heartbeat} {Interval} {Time} {Series} during {Regular} {Walking}},
  volume = {19},
  doi = {10.3390/e19100568},
  journal = {Entropy},
  author = {Shi, Bo and Zhang, Yudong and Yuan, Chaochao and Wang, Shuihua and Li, Peng},
  year = {2017},
  pages = {568},
  annote = {The following values have no corresponding Zotero field:number: 10}
}

@article{wang_pathological_2017,
  title = {Pathological brain detection via wavelet packet tsallis entropy and real-coded biogeography-based optimization},
  volume = {151},
  issn = {0169-2968},
  doi = {10.3233/fi-2017-1492},
  language = {English},
  journal = {Fundamenta Informaticae},
  author = {Wang, Shuihua and Li, Peng and Chen, Peng and Phillips, Preetha and Liu, Ge and Du, Sidan and Zhang, Yudong},
  year = {2017},
  keywords = {algorithms, artificial neural-networks, classification, Computer Science, feed-forward neural network, images, Mathematics, mri, optimal power-flow, packet Tsallis entropy, pathological brain detection, prostate-cancer, real-coded biogeography-based optimization, segmentation, support vector machine, system, wavelet},
  pages = {275--91},
  annote = {The following values have no corresponding Zotero field:auth-address: [Wang, Shuihua{\textbar}Du, Sidan{\textbar}Zhang, Yudong] Nanjing Univ, Sch Elect Sci \& Engn, Nanjing 210046, Jiangsu, Peoples R China. [Li, Peng] Shandong Univ, Sch Control Sci \& Engn, Jinan 205100, Peoples R China. [Chen, Peng] Columbia Univ, Dept Elect Engn, New York, NY 10027 USA. [Phillips, Preetha] Shepherd Univ, Sch Nat Sci \& Math, Shepherdstown, WV 25443 USA. [Liu, Ge] Columbia Univ, Coll Phys \& Surg, Dept Psychiat, New York, NY 10032 USA. [Wang, Shuihua] Nanjing Normal Univ, Sch Comp Sci \& Technol, Nanjing 210023, Jiangsu, Peoples R China. Zhang, YD (reprint author), Nanjing Univ, Sch Elect Sci \& Engn, Nanjing 210046, Jiangsu, Peoples R China. coff128@nju.edu.cn{\textbar}zhangyudong@njnu.edu.cnalt-title: Fundam. Inform.number: 1-4accession-num: WOS:000398583500017work-type: Article}
}

(Aim) In order to detect pathological brains in a more efficient way, (Method) we proposed a novel system of pathological brain detection (PBD) that combined wavelet packet Tsallis entropy (WPTE), feedforward neural network (FNN), and real-coded biogeography-based optimization (RCBBO). (Results) The experiments showed the proposed WPTE + FNN + RCBBO approach yielded an average accuracy of 99.49% over a 255-image dataset. (Conclusions) The WPTE + FNN + RCBBO performed better than 10 state-of-the-art approaches.

@article{li_classification_2016,
  title = {Classification of five-second epileptic {EEG} recordings using distribution entropy and sample entropy},
  volume = {7},
  issn = {1664-042X},
  shorttitle = {Classification of {Epileptic} {EEG} {Using} {DistEn} and {SampEn}},
  doi = {10.3389/fphys.2016.00136},
  language = {English},
  journal = {Frontiers in Physiology},
  author = {Li, Peng and Karmakar, Chandan and Yan, Chang and Palaniswami, Marimuthu and Liu, Changchun},
  month = apr,
  year = {2016},
  pmid = {27148074. PMCID: PMC4830849},
  keywords = {electroencephalogram (EEG),epileptic seizure,Sample entropy (SampEn),distribution entropy (DistEn),short-length EEG analysis},
  pages = {136},
  annote = {The following values have no corresponding Zotero field:auth-address: (Dr Peng Li,Shandong University,School of Control Science and Engineering,Jinan,250061,China,pli@sdu.edu.cn) (Dr Chandan Karmakar,Deakin University,Centre of Pattern Recognition and Data Analytics (PRaDA),Geelong,3220,VIC,Australia,karmakar@deakin.edu.au) (Mr Chang Yan,cyan@mail.sdu.edu.cn) (Prof Marimuthu Palaniswami,University of Melbourne,Electrical \& Electronic Engineering Department,Melbourne,3010,VIC,Australia,palani@unimelb.edu.au) (Prof Changchun Liu,lskyp@mail.sdu.edu.cn)abbr-2: Front. Physiol.}
}

Epilepsy is an electrophysiological disorder of the brain, the hallmark of which is recurrent and unprovoked seizures. Electroencephalogram (EEG) measures electrical activity of the brain that is commonly applied as a non-invasive technique for seizure detection. Although a vast number of publications have been published on intelligent algorithms to classify interictal and ictal EEG, it remains an open question whether they can be detected using short-length EEG recordings. In this study, we proposed three protocols to select 5 s EEG segment for classifying interictal and ictal EEG from normal. We used the publicly-accessible Bonn database, which consists of normal, interical, and ictal EEG signals with a length of 4097 sampling points (23.6 s) per record. In this study, we selected three segments of 868 points (5 s) length from each recordings and evaluated results for each of them separately. The well-studied irregularity measure—sample entropy (SampEn)—and a more recently proposed complexity measure—distribution entropy (DistEn)—were used as classification features. A total of 20 combinations of input parameters m and τ for the calculation of SampEn and DistEn were selected for compatibility. Results showed that SampEn was undefined for half of the used combinations of input parameters and indicated a large intra-class variance. Moreover, DistEn performed robustly for short-length EEG data indicating relative independence from input parameters and small intra-class fluctuations. In addition, it showed acceptable performance for all three classification problems (interictal EEG from normal, ictal EEG from normal, and ictal EEG from interictal) compared to SampEn, which showed better results only for distinguishing normal EEG from interictal and ictal. Both SampEn and DistEn showed good reproducibility and consistency, as evidenced by the independence of results on analysing protocol.

@article{ji_measuring_2016,
  title = {Measuring electromechanical coupling in patients with coronary artery disease and healthy subjects},
  volume = {18},
  issn = {1099-4300},
  doi = {10.3390/e18040153},
  language = {English},
  journal = {Entropy},
  author = {Ji, Lizhen and Li, Peng and Liu, Chengyu and Wang, Xinpei and Yang, Jing and Liu, Changchun},
  month = apr,
  year = {2016},
  keywords = {electrocardiogram, sample entropy, CONDITIONAL ENTROPY, coronary artery disease, coupling, CROSS-ENTROPY MEASURES, diastolic time interval, entropy-based measurement, heartbeat interval, LEFT-VENTRICULAR HYPERTROPHY, MEASURING SYNCHRONIZATION, MYOCARDIAL-INFARCTION, PATTERN SYNCHRONIZATION, photoplethysmography, SIGNALS, SYSTEMS, systolic time interval, TIME-SERIES},
  pages = {153},
  annote = {The following values have no corresponding Zotero field:auth-address: Shandong Univ, Sch Control Sci \& Engn, Jingshi Rd 17923, Jinan 250061, Peoples R China{\textbar}Shandong Univ, Sch Comp Sci \& Technol, Shunhua Rd 1500, Jinan 250101, Peoples R Chinanumber: 4accession-num: WOS:000375208200050}
}

Coronary artery disease (CAD) is the most common cause of death globally. To detect CAD noninvasively at an early stage before clinical symptoms occur is still nowadays challenging. Analysis of the variation of heartbeat interval (RRI) opens a new avenue for evaluating the functional change of cardiovascular system which is accepted to occur at the subclinical stage of CAD. In addition, systolic time interval (STI) and diastolic time interval (DTI) also show potential. There may be coupling in these electromechanical time series due to their physiological connection. However, to the best of our knowledge no publication has systematically investigated how can the coupling be measured and how it changes in CAD patients. In this study, we enrolled 39 CAD patients and 36 healthy subjects and for each subject the electrocardiogram (ECG) and photoplethysmography (PPG) signals were recorded simultaneously for 5 min. The RRI series, STI series, and DTI series were constructed, respectively. We used linear cross correlation (CC), coherence function (CF), as well as nonlinear mutual information (MI), cross conditional entropy (XCE), cross sample entropy (XSampEn), and cross fuzzy entropy (XFuzzyEn) to analyse the bivariate RRI-DTI coupling, RRI-STI coupling, and STI-DTI coupling, respectively. Our results suggest that the linear CC and CF generally have no significant difference between the two groups for all three types of bivariate coupling. The MI only shows weak change in RRI-DTI coupling. By comparison, the three entropy-based coupling measurements show significantly decreased coupling in CAD patients except XSampEn for RRI-DTI coupling (less significant) and XCE for STI-DTI and RRI-STI coupling (not significant). Additionally, the XFuzzyEn performs best as it was still significant if we further applied the Bonferroni correction in our statistical analysis. Our study indicates that the intrinsic electromechanical coupling is most probably nonlinear and can better be measured by nonlinear entropy-based measurements especially the XFuzzyEn. Besides, CAD patients are accompanied by a loss of electromechanical coupling. Our results suggest that cardiac electromechanical coupling may potentially serve as a noninvasive diagnostic tool for CAD.

@article{li_detection_2016,
  title = {Detection of coupling in short physiological series by a joint distribution entropy method},
  volume = {63},
  issn = {1558-2531 (Electronic) 0018-9294 (Linking)},
  doi = {10.1109/TBME.2016.2515543},
  number = {11},
  journal = {IEEE Transactions on Biomedical Engineering},
  author = {Li, Peng and Li, Ke and Liu, Chengyu and Zheng, Dingchang and Li, Zong-Ming and Liu, Changchun},
  month = jan,
  year = {2016},
  pmid = {26760967},
  pages = {2231--42},
  annote = {The following values have no corresponding Zotero field:abbr-2: IEEE Trans. Biomed. Eng.accession-num: 26760967}
}

OBJECTIVE: In this study we developed a joint distribution entropy (JDistEn) method to robustly estimate the coupling in short physiological series. METHODS: The JDistEn method is derived from a joint distance matrix which is constructed from a combination of the distance matrix corresponding to each individual data channel using a geometric mean calculation. A coupled Rossler system and a coupled dual-kinetics neural mass model were used to examine how well JDistEn performed, specifically, its sensitivity for detecting weak coupling, its consistency in gauging coupling strength, and its reliability in processing input of decreased data length. Performance of JDistEn in estimating physiological coupling was further examined with bivariate electroencephalography (EEG) data from rats and RR interval and diastolic time interval (RRI-DTI) series from human beings. Cross-sample entropy (XSampEn), cross-conditional entropy (XCE), and Shannon entropy of diagonal lines in the joint recurrence plots (JENT) were applied for purposes of comparison. RESULTS: Simulation results suggest that JDistEn showed markedly higher sensitivity than XSampEn, XCE, and JENT for dynamics in weak coupling, although as the simulation models were more intensively coupled, JDistEn performance was comparable to the three others. In addition, this improved sensitivity was much more pronounced for short data sets. Experimental results further confirmed that JDistEn outperformed XSampEn, XCE, and JENT for detecting weak coupling, especially for short physiological data. CONCLUSION: this study suggested that our proposed JDistEn could be useful for continuous and even real-time coupling analysis for physiological signals in clinical practice.

@article{hu_progression_2016,
  title = {Progression of dementia assessed by temporal correlations of physical activity: {Results} from a 3.5-year, longitudinal randomized controlled trial},
  volume = {6},
  issn = {2045-2322 (Electronic) 2045-2322 (Linking)},
  doi = {10.1038/srep27742},
  journal = {Scientific Report},
  author = {Hu, K. and Riemersma-van der Lek, R. F. and Patxot, M. and Li, P. and Shea, S. A. and Scheer, F. A. and Van Someren, E. J.},
  year = {2016},
  pmid = {27292543. PMCID: PMC4904193},
  pages = {27742},
  annote = {The following values have no corresponding Zotero field:auth-address: Division of Sleep and Circadian Disorders, Brigham and Women's Hospital; and Division of Sleep Medicine, Harvard Medical School, Boston, MA 02215, United States. Netherlands Institute for Neuroscience, Amsterdam, The Netherlands. University of Groningen, University Medical Center Groningen, Department of Psychiatry, Groningen, The Netherlands. Oregon Institute of Occupational Health Sciences, Oregon Health \&Science University, Portland, OR 97239, United States. Depts. of Integrative Neurophysiology and Psychiatry GGZ inGeest, Center for Neurogenomics and Cognitive Research (CNCR), Neuroscience Campus Amsterdam, VU University and Medical Center, Amsterdam, The Netherlands.abbr-2: Sci. Rep.accession-num: 27292543}
}

Cross-sectional studies show that activity fluctuations in healthy young adults possess robust temporal correlations that become altered with aging, and in dementia and depression. This study was designed to test whether or not within-subject changes of activity correlations (i) track the clinical progression of dementia, (ii) reflect the alterations of depression symptoms in patients with dementia, and (iii) can be manipulated by clinical interventions aimed at stabilizing circadian rhythmicity and improving sleep in dementia, namely timed bright light therapy and melatonin supplementation. We examined 144 patients with dementia (70-96 years old) who were assigned to daily treatment with bright light, bedtime melatonin, both or placebos only in a 3.5-year double-blinded randomized clinical trial. We found that activity correlations at temporal scales \textless~2 hours significantly decreased over time and that light treatment attenuated the decrease by ~73%. Moreover, the decrease of temporal activity correlations positively correlated with the degrees of cognitive decline and worsening of mood though the associations were relatively weak. These results suggest a mechanistic link between multiscale activity regulation and circadian/sleep function in dementia patients. Whether temporal activity patterns allow unobtrusive, long-term monitoring of dementia progression and mood changes is worth further investigation.

@article{ji_analysis_2016,
  title = {Analysis of cardiac electro-mechanical time-series in patients with coronary artery disease based on entropy},
  volume = {52},
  shorttitle = {Comput {Eng} {App}},
  journal = {Computer Engineering and Applications},
  author = {Ji, Lizhen and Li, Peng and Li, Lin and Liu, Chengyu and Wang, Xinpei and Li, Ke and Liu, Changchun},
  year = {2016},
  pages = {265--70},
  annote = {The following values have no corresponding Zotero field:number: 10work-type: (in Chinese)}
}

@article{ji_comparison_2015,
  title = {Comparison of heart rate variability between resting state and external-cuff-inflation-and-deflation state: a pilot study},
  volume = {36},
  issn = {0967-3334;1361-6579},
  doi = {10.1088/0967-3334/36/10/2135},
  number = {10},
  journal = {Physiological Measurement},
  author = {Ji, Lizhen and Liu, Chengyu and Li, Peng and Wang, Xinpei and Yan, Chang and Liu, Changchun},
  month = oct,
  year = {2015},
  pmid = {26333766},
  pages = {2135--46},
  annote = {The following values have no corresponding Zotero field:number: 10accession-num: WOS:000367690500010}
}

@article{ji_analysis_2015,
  title = {Analysis of short-term heart rate and diastolic period variability using a refined fuzzy entropy method},
  volume = {14},
  issn = {1475-925X},
  doi = {10.1186/s12938-015-0063-z},
  journal = {Biomedical Engineering Online},
  author = {Ji, Lizhen and Li, Peng and Li, Ke and Wang, Xinpei and Liu, Changchun},
  month = jul,
  year = {2015},
  pmid = {26126807. PMCID: PMC4487860},
  pages = {64},
  annote = {The following values have no corresponding Zotero field:accession-num: WOS:000357076900001}
}

@article{li_assessing_2015,
  title = {Assessing the complexity of short-term heartbeat interval series by distribution entropy},
  volume = {53},
  issn = {0140-0118;1741-0444},
  doi = {10.1007/s11517-014-1216-0},
  journal = {Medical \& Biological Engineering \& Computing},
  author = {Li, Peng and Liu, Chengyu and Li, Ke and Zheng, Dingchang and Liu, Changchun and Hou, Yinglong},
  month = jan,
  year = {2015},
  pmid = {25351477},
  pages = {77--87},
  annote = {The following values have no corresponding Zotero field:abbr-2: Med. Biol. Eng. Comput.number: 1accession-num: WOS:000347161800008}
}

@article{liu_multi-step_2014,
  title = {A multi-step method with signal quality assessment and fine-tuning procedure to locate maternal and fetal {QRS} complexes from abdominal {ECG} recordings},
  volume = {35},
  issn = {0967-3334;1361-6579},
  doi = {10.1088/0967-3334/35/8/1665},
  journal = {Physiological Measurement},
  author = {Liu, Chengyu and Li, Peng and Di Maria, Costanzo and Zhao, Lina and Zhang, Henggui and Chen, Zhiqing},
  month = aug,
  year = {2014},
  pmid = {25069817},
  pages = {1665--83},
  annote = {The following values have no corresponding Zotero field:number: 8accession-num: WOS:000343765300010}
}

@article{li_low-complexity_2014,
  title = {A low-complexity data-adaptive approach for premature ventricular contraction recognition},
  volume = {8},
  issn = {1863-1703;1863-1711},
  doi = {10.1007/s11760-013-0478-6},
  journal = {Signal Image and Video Processing},
  author = {Li, Peng and Liu, Chengyu and Wang, Xinpei and Zheng, Dingchang and Li, Yuanyang and Liu, Changchun},
  month = jan,
  year = {2014},
  pages = {111--20},
  annote = {The following values have no corresponding Zotero field:number: 1accession-num: WOS:000329357900010}
}

@article{sun_influence_2014,
  title = {Influence of timing algorithm on brachial-ankle pulse wave velocity measurement},
  volume = {24},
  issn = {0959-2989;1878-3619},
  doi = {10.3233/BME-130806},
  journal = {Bio-Medical Materials and Engineering},
  author = {Sun, Xin and Li, Ke and Ren, Hongwei and Li, Peng and Wang, Xinpei and Liu, Changchun},
  year = {2014},
  pmid = {24211905},
  pages = {255--61},
  annote = {The following values have no corresponding Zotero field:abbr-2: Bio-Med. Mater. Eng.number: 1accession-num: WOS:000327312600031}
}

@article{he_refining_2014,
  title = {Refining of the membership function in cross fuzzy entropy and its influence},
  volume = {44},
  journal = {Journal of Shandong University (Engineering Science)},
  author = {He, Siyan and Li, Peng and Liu, Chengyu and Wu, Xueqian and Chen, Qijun},
  year = {2014},
  pages = {63--8},
  annote = {The following values have no corresponding Zotero field:number: 1}
}

@article{li_testing_2013,
  title = {Testing pattern synchronization in coupled systems through different entropy-based measures},
  volume = {51},
  issn = {0140-0118},
  doi = {10.1007/s11517-012-1028-z},
  journal = {Medical \& Biological Engineering \& Computing},
  author = {Li, Peng and Liu, Chengyu and Wang, Xinpei and Li, Liping and Yang, Lei and Chen, Yongcai and Liu, Changchun},
  month = may,
  year = {2013},
  pmid = {23337958},
  pages = {581--91},
  annote = {The following values have no corresponding Zotero field:abbr-2: Med. Biol. Eng. Comput.number: 5accession-num: WOS:000317844500010}
}

@article{li_multiscale_2013,
  title = {Multiscale multivariate fuzzy entropy analysis},
  volume = {62},
  journal = {Acta Physica Sinica},
  author = {Li, Peng and Liu, Chengyu and Li, Liping and Ji, Lizhen and Yu, Shouyuan and Liu, Changchun},
  year = {2013},
  pages = {120512},
  annote = {The following values have no corresponding Zotero field:abbr-2: Acta Phys. Sin.number: 12accession-num: SCI, IF: 1.027}
}

@article{__2013,
  title = {多尺度多变量模糊熵分析},
  url = {http://www.cqvip.com/qk/94684x/201312/46227971.html},
  number = {12},
  urldate = {2021-10-18},
  journal = {物理学报},
  author = {李鹏 and 刘澄玉 and 李丽萍 and 纪丽珍 and 于守元 and 刘常春},
  year = {2013},
  pages = {122--130},
  file = {Snapshot:C\:\\Users\\pl806\\Zotero\\storage\\FUBR9F4D\\46227971.html:text/html}
}

@article{liu_elastic_2013,
  title = {Elastic properties of peripheral arteries in heart failure patients in comparison with normal subjects},
  volume = {63},
  doi = {10.1007/s12576-013-0254-y},
  journal = {Journal of Physiological Sciences},
  author = {Liu, Chengyu and Zheng, Dingchang and Zhao, Lina and Li, Peng and Li, Bin and Murray, Alan and Liu, Changchun},
  year = {2013},
  pmid = {23519698},
  pages = {195--201},
  annote = {The following values have no corresponding Zotero field:number: 3}
}

@article{liu_combination_2012,
  title = {A combination method of improved impulse rejection filter and template matching for identification of anomalous intervals in {RR} sequences},
  volume = {32},
  journal = {Journal of Medical and Biological Engineering},
  author = {Liu, Chengyu and Li, Liping and Zhao, Lina and Zheng, Dingchang and Li, Peng and Liu, Changchun},
  year = {2012},
  pages = {245--50},
  annote = {The following values have no corresponding Zotero field:abbr-2: J. Med. Biol. Eng.number: 4}
}

@article{he_acquisition_2012,
  title = {An acquisition technology of apex-cardiogram based on sensor array and hyperbolic position model},
  volume = {31},
  shorttitle = {Beijing {Biomed} {Eng}},
  journal = {Beijing Biomedical Engineering},
  author = {He, Siyan and Liu, Chengyu and Zhang, Yuan and Zhao, Lina and Li, Peng},
  year = {2012},
  pages = {361--5},
  annote = {The following values have no corresponding Zotero field:number: 4}
}

@article{_qrs_2011,
  title = {{一种QRS波群实时检测方法}},
  volume = {27},
  url = {http://www.cqvip.com/qk/91590x/201103/37670165.html},
  number = {3},
  urldate = {2021-10-18},
  journal = {生物物理学报},
  author = {李鹏 and 刘常春 and 张明 and 车文彪 and 李键},
  year = {2011},
  pages = {222--230},
  file = {Snapshot:C\:\\Users\\pl806\\Zotero\\storage\\JMDNG7N8\\37670165.html:text/html}
}

@article{li_real-time_2011,
  title = {A real-time {QRS} complex detection method},
  volume = {27},
  journal = {Acta Biophysica Sinica},
  author = {Li, Peng and Liu, Changchun and Zhang, Ming and Che, Wenbiao and Li, Jian},
  year = {2011},
  pages = {222--30},
  annote = {The following values have no corresponding Zotero field:abbr-2: Acta Biophys. Sin.number: 3}
}

@article{cao_application_2010,
  title = {Application of bilinear interpolation algorithm in image rotation based on {MATLAB}},
  volume = {2},
  journal = {China Printing and Packaging Study},
  author = {Cao, Dianguo and Chen, Haojie and Li, Peng},
  year = {2010},
  pages = {74--8},
  annote = {The following values have no corresponding Zotero field:number: 4}
}