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Peng, Chung-Kang

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Chung-Kang

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Peng, Chung-Kang
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    Outlier-resilient complexity analysis of heartbeat dynamics
    (Nature Publishing Group, 2015) Lo, Men-Tzung; Chang, Yi-Chung; Lin, Chen; Young, Hsu-Wen Vincent; Lin, Yen-Hung; Ho, Yi-Lwun; Peng, Chung-Kang; Hu, Kun
    Complexity in physiological outputs is believed to be a hallmark of healthy physiological control. How to accurately quantify the degree of complexity in physiological signals with outliers remains a major barrier for translating this novel concept of nonlinear dynamic theory to clinical practice. Here we propose a new approach to estimate the complexity in a signal by analyzing the irregularity of the sign time series of its coarse-grained time series at different time scales. Using surrogate data, we show that the method can reliably assess the complexity in noisy data while being highly resilient to outliers. We further apply this method to the analysis of human heartbeat recordings. Without removing any outliers due to ectopic beats, the method is able to detect a degradation of cardiac control in patients with congestive heart failure and a more degradation in critically ill patients whose life continuation relies on extracorporeal membrane oxygenator (ECMO). Moreover, the derived complexity measures can predict the mortality of ECMO patients. These results indicate that the proposed method may serve as a promising tool for monitoring cardiac function of patients in clinical settings.
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    A Strategy to Reduce Bias of Entropy Estimates in Resting-State fMRI Signals
    (Frontiers Media S.A., 2018) Yang, Albert C.; Tsai, Shih-Jen; Lin, Ching-Po; Peng, Chung-Kang
    Complexity analysis of resting-state blood oxygen level-dependent (BOLD) signals using entropy methods has attracted considerable attention. However, investigation on the bias of entropy estimates in resting-state functional magnetic resonance imaging (fMRI) signals and a general strategy for selecting entropy parameters is lacking. In this paper, we present a minimizing error approach to reduce the bias of sample entropy (SampEn) and multiscale entropy (MSE) in resting-state fMRI data. The strategy explored a range of parameters that minimized the relative error of SampEn of BOLD signals in cerebrospinal fluids where minimal physiologic information was present, and applied these parameters to calculate SampEn of BOLD signals in gray matter regions. We examined the effect of various parameters on the results of SampEn and MSE analyses of a large normal aging adult cohort (354 healthy subjects aged 21–89 years). The results showed that a tradeoff between pattern length m and tolerance factor r was necessary to maintain the accuracy of SampEn estimates. Furthermore, an increased relative error of SampEn was associated with an increased coefficient of variation in voxel-wise statistics. Overall, the parameters m = 1 and r = 0.20–0.45 provided reliable MSE estimates in short resting-state fMRI signals. For a single-scale SampEn analysis, a wide range of parameters was available with data lengths of at least 97 time points. This study provides a minimization error strategy for future studies on the non-linear analysis of resting-state fMRI signals to account for the bias of entropy estimates.
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    Multi-Scale Glycemic Variability: A Link to Gray Matter Atrophy and Cognitive Decline in Type 2 Diabetes
    (Public Library of Science, 2014) Cui, Xingran; Abduljalil, Amir; Manor, Brad D.; Peng, Chung-Kang; Novak, Vera
    Objective: Type 2 diabetes mellitus (DM) accelerates brain aging and cognitive decline. Complex interactions between hyperglycemia, glycemic variability and brain aging remain unresolved. This study investigated the relationship between glycemic variability at multiple time scales, brain volumes and cognition in type 2 DM. Research Design and Methods Forty-three older adults with and 26 without type 2 DM completed 72-hour continuous glucose monitoring, cognitive tests and anatomical MRI. We described a new analysis of continuous glucose monitoring, termed Multi-Scale glycemic variability (Multi-Scale GV), to examine glycemic variability at multiple time scales. Specifically, Ensemble Empirical Mode Decomposition was used to identify five unique ultradian glycemic variability cycles (GVC1–5) that modulate serum glucose with periods ranging from 0.5–12 hrs. Results: Type 2 DM subjects demonstrated greater variability in GVC3–5 (period 2.0–12 hrs) than controls (P<0.0001), during the day as well as during the night. Multi-Scale GV was related to conventional markers of glycemic variability (e.g. standard deviation and mean glycemic excursions), but demonstrated greater sensitivity and specificity to conventional markers, and was associated with worse long-term glycemic control (e.g. fasting glucose and HbA1c). Across all subjects, those with greater glycemic variability within higher frequency cycles (GVC1–3; 0.5–2.0 hrs) had less gray matter within the limbic system and temporo-parietal lobes (e.g. cingulum, insular, hippocampus), and exhibited worse cognitive performance. Specifically within those with type 2 DM, greater glycemic variability in GVC2–3 was associated with worse learning and memory scores. Greater variability in GVC5 was associated with longer DM duration and more depression. These relationships were independent of HbA1c and hypoglycemic episodes. Conclusions: Type 2 DM is associated with dysregulation of glycemic variability over multiple scales of time. These time-scale-dependent glycemic fluctuations might contribute to brain atrophy and cognitive outcomes within this vulnerable population.
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    Reversible heart rhythm complexity impairment in patients with primary aldosteronism
    (Nature Publishing Group, 2015) Lin, Yen-Hung; Wu, Vin-Cent; Lo, Men-Tzung; Wu, Xue-Ming; Hung, Chi-Sheng; Wu, Kwan-Dun; Lin, Chen; Ho, Yi-Lwun; Stowasser, Michael; Peng, Chung-Kang
    Excess aldosterone secretion in patients with primary aldosteronism (PA) impairs their cardiovascular system. Heart rhythm complexity analysis, derived from heart rate variability (HRV), is a powerful tool to quantify the complex regulatory dynamics of human physiology. We prospectively analyzed 20 patients with aldosterone producing adenoma (APA) that underwent adrenalectomy and 25 patients with essential hypertension (EH). The heart rate data were analyzed by conventional HRV and heart rhythm complexity analysis including detrended fluctuation analysis (DFA) and multiscale entropy (MSE). We found APA patients had significantly decreased DFAα2 on DFA analysis and decreased area 1–5, area 6–15, and area 6–20 on MSE analysis (all p < 0.05). Area 1–5, area 6–15, area 6–20 in the MSE study correlated significantly with log-transformed renin activity and log-transformed aldosterone-renin ratio (all p < = 0.01). The conventional HRV parameters were comparable between PA and EH patients. After adrenalectomy, all the altered DFA and MSE parameters improved significantly (all p < 0.05). The conventional HRV parameters did not change. Our result suggested that heart rhythm complexity is impaired in APA patients and this is at least partially reversed by adrenalectomy.
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    Complexity of cardiac signals for predicting changes in alpha-waves after stress in patients undergoing cardiac catheterization
    (Nature Publishing Group, 2015) Chiu, Hung-Chih; Lin, Yen-Hung; Lo, Men-Tzung; Tang, Sung-Chun; Wang, Tzung-Dau; Lu, Hung-Chun; Ho, Yi-Lwun; Ma, Hsi-Pin; Peng, Chung-Kang
    The hierarchical interaction between electrical signals of the brain and heart is not fully understood. We hypothesized that the complexity of cardiac electrical activity can be used to predict changes in encephalic electricity after stress. Most methods for analyzing the interaction between the heart rate variability (HRV) and electroencephalography (EEG) require a computation-intensive mathematical model. To overcome these limitations and increase the predictive accuracy of human relaxing states, we developed a method to test our hypothesis. In addition to routine linear analysis, multiscale entropy and detrended fluctuation analysis of the HRV were used to quantify nonstationary and nonlinear dynamic changes in the heart rate time series. Short-time Fourier transform was applied to quantify the power of EEG. The clinical, HRV, and EEG parameters of postcatheterization EEG alpha waves were analyzed using change-score analysis and generalized additive models. In conclusion, the complexity of cardiac electrical signals can be used to predict EEG changes after stress.
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    Multi-scale symbolic entropy analysis provides prognostic prediction in patients receiving extracorporeal life support
    (BioMed Central, 2014) Lin, Yen-Hung; Huang, Hui-Chun; Chang, Yi-Chung; Lin, Chen; Lo, Men-Tzung; Liu, Li-Yu Daisy; Tsai, Pi-Ru; Chen, Yih-Sharng; Ko, Wen-Je; Ho, Yi-Lwun; Chen, Ming-Fong; Peng, Chung-Kang; Buchman, Timothy G
    Introduction: Extracorporeal life support (ECLS) can temporarily support cardiopulmonary function, and is occasionally used in resuscitation. Multi-scale entropy (MSE) derived from heart rate variability (HRV) is a powerful tool in outcome prediction of patients with cardiovascular diseases. Multi-scale symbolic entropy analysis (MSsE), a new method derived from MSE, mitigates the effect of arrhythmia on analysis. The objective is to evaluate the prognostic value of MSsE in patients receiving ECLS. The primary outcome is death or urgent transplantation during the index admission. Methods: Fifty-seven patients receiving ECLS less than 24 hours and 23 control subjects were enrolled. Digital 24-hour Holter electrocardiograms were recorded and three MSsE parameters (slope 5, Area 6–20, Area 6–40) associated with the multiscale correlation and complexity of heart beat fluctuation were calculated. Results: Patients receiving ECLS had significantly lower value of slope 5, area 6 to 20, and area 6 to 40 than control subjects. During the follow-up period, 29 patients met primary outcome. Age, slope 5, Area 6 to 20, Area 6 to 40, acute physiology and chronic health evaluation II score, multiple organ dysfunction score (MODS), logistic organ dysfunction score (LODS), and myocardial infarction history were significantly associated with primary outcome. Slope 5 showed the greatest discriminatory power. In a net reclassification improvement model, slope 5 significantly improved the predictive power of LODS; Area 6 to 20 and Area 6 to 40 significantly improved the predictive power in MODS. In an integrated discrimination improvement model, slope 5 added significantly to the prediction power of each clinical parameter. Area 6 to 20 and Area 6 to 40 significantly improved the predictive power in sequential organ failure assessment. Conclusions: MSsE provides additional prognostic information in patients receiving ECLS. Electronic supplementary material The online version of this article (doi:10.1186/s13054-014-0548-3) contains supplementary material, which is available to authorized users.
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    Tai Chi Training may Reduce Dual Task Gait Variability, a Potential Mediator of Fall Risk, in Healthy Older Adults: Cross-Sectional and Randomized Trial Studies
    (Frontiers Media S.A., 2015) Wayne, Peter; Hausdorff, Jeffrey M.; Lough, Matthew; Gow, Brian J.; Lipsitz, Lewis; Novak, Vera; Macklin, Eric; Peng, Chung-Kang; Manor, Brad
    Background: Tai Chi (TC) exercise improves balance and reduces falls in older, health-impaired adults. TC’s impact on dual task (DT) gait parameters predictive of falls, especially in healthy active older adults, however, is unknown. Purpose To compare differences in usual and DT gait between long-term TC-expert practitioners and age-/gender-matched TC-naïve adults, and to determine the effects of short-term TC training on gait in healthy, non-sedentary older adults. Methods: A cross-sectional study compared gait in healthy TC-naïve and TC-expert (24.5 ± 12 years experience) older adults. TC-naïve adults then completed a 6-month, two-arm, wait-list randomized clinical trial of TC training. Gait speed and stride time variability (Coefficient of Variation %) were assessed during 90 s trials of undisturbed and cognitive DT (serial subtractions) conditions. Results: During DT, gait speed decreased (p < 0.003) and stride time variability increased (p < 0.004) in all groups. Cross-sectional comparisons indicated that stride time variability was lower in the TC-expert vs. TC-naïve group, significantly so during DT (2.11 vs. 2.55%; p = 0.027); by contrast, gait speed during both undisturbed and DT conditions did not differ between groups. Longitudinal analyses of TC-naïve adults randomized to 6 months of TC training or usual care identified improvement in DT gait speed in both groups. A small improvement in DT stride time variability (effect size = 0.2) was estimated with TC training, but no significant differences between groups were observed. Potentially important improvements after TC training could not be excluded in this small study. Conclusion: In healthy active older adults, positive effects of short- and long-term TC were observed only under cognitively challenging DT conditions and only for stride time variability. DT stride time variability offers a potentially sensitive metric for monitoring TC’s impact on fall risk with healthy older adults.
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    Obstructive Sleep Apnea Alters Sleep Stage Transition Dynamics
    (Public Library of Science (PLoS), 2010) Bianchi, Matt Travis; Cash, Sydney; Mietus, Joseph; Peng, Chung-Kang; Thomas, Robert
    Introduction: Enhanced characterization of sleep architecture, compared with routine polysomnographic metrics such as stage percentages and sleep efficiency, may improve the predictive phenotyping of fragmented sleep. One approach involves using stage transition analysis to characterize sleep continuity. Methods and Principal Findings: We analyzed hypnograms from Sleep Heart Health Study (SHHS) participants using the following stage designations: wake after sleep onset (WASO), non-rapid eye movement (NREM) sleep, and REM sleep. We show that individual patient hypnograms contain insufficient number of bouts to adequately describe the transition kinetics, necessitating pooling of data. We compared a control group of individuals free of medications, obstructive sleep apnea (OSA), medical co-morbidities, or sleepiness (n = 374) with mild (n = 496) or severe OSA (n = 338). WASO, REM sleep, and NREM sleep bout durations exhibited multi-exponential temporal dynamics. The presence of OSA accelerated the “decay” rate of NREM and REM sleep bouts, resulting in instability manifesting as shorter bouts and increased number of stage transitions. For WASO bouts, previously attributed to a power law process, a multi-exponential decay described the data well. Simulations demonstrated that a multi-exponential process can mimic a power law distribution. Conclusion and Significance: OSA alters sleep architecture dynamics by decreasing the temporal stability of NREM and REM sleep bouts. Multi-exponential fitting is superior to routine mono-exponential fitting, and may thus provide improved predictive metrics of sleep continuity. However, because a single night of sleep contains insufficient transitions to characterize these dynamics, extended monitoring of sleep, probably at home, would be necessary for individualized clinical application.
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    Cardiac Autonomic Alteration and Metabolic Syndrome: An Ambulatory ECG-based Study in A General Population
    (Nature Publishing Group, 2017) Ma, Yan; Tseng, Ping-Huei; Ahn, Andrew; Wu, Ming-Shiang; Ho, Yi-Lwun; Chen, Ming-Fong; Peng, Chung-Kang
    Metabolic syndrome (MetS) has been associated with chronic damage to the cardiovascular system. This study aimed to evaluate early stage cardiac autonomic dysfunction with electrocardiography (ECG)-based measures in MetS subjects. During 2012–2013, 175 subjects with MetS and 226 healthy controls underwent ECG recordings of at least 4 hours starting in the morning with ambulatory one-lead ECG monitors. MetS was diagnosed using the criteria defined in the Adult Treatment Panel III, with a modification of waist circumference for Asians. Conventional heart rate variability (HRV) analysis, and complexity index (CI1–20) calculated from 20 scales of entropy (multiscale entropy, MSE), were compared between subjects with MetS and controls. Compared with the healthy controls, subjects with MetS had significantly reduced HRV, including SDNN and pNN20 in time domain, VLF, LF and HF in frequency domain, as well as SD2 in Poincaré analysis. MetS subjects have significantly lower complexity index (CI1–20) than healthy subjects (1.69 ± 0.18 vs. 1.77 ± 0.12, p < 0.001). MetS severity was inversely associated with the CI1–20 (r = −0.27, p < 0.001). MetS is associated with significant alterations in heart rate dynamics, including HRV and complexity.
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    A Higher Proportion of Metabolic Syndrome in Chinese Subjects with Sleep-Disordered Breathing: A Case-Control Study Based on Electrocardiogram-Derived Sleep Analysis
    (Public Library of Science, 2017) Tseng, Ping-Huei; Lee, Pei-Lin; Hsu, Wei-Chung; Ma, Yan; Lee, Yi-Chia; Chiu, Han-Mo; Ho, Yi-Lwun; Chen, Ming-Fong; Wu, Ming-Shiang; Peng, Chung-Kang
    Objective: The prevalence of metabolic syndrome (MS) has increased rapidly in Taiwan and worldwide. We aim to determine the association between sleep-disordered breathing (SDB) and MS in a Chinese general population. Methods: This case-control study recruited subjects who have undergone a prospective electrocardiogram-based cardiopulmonary coupling (CPC) sleep spectrogram as part of the periodic health check-ups at the National Taiwan University Hospital. Comprehensive anthropometrics, blood biochemistry, prevalence of MS and its individual components were compared with Bonferroni correction between 40 subjects with SDB, defined as the CPC-derived apnea–hypopnea index (CPC-AHI) >5 event/hour and 80 age- and sex-matched controls, defined as CPC-AHI <1 event/hour. MS was diagnosed based on the Adult Treatment Panel III, with a modification of waist circumference for Asians. Results: Subjects with SDB were more obese with larger waist circumferences (95.1±12.9 vs. 87.3±6.9, P < .001) and borderline higher BMI (27.0±4.9 vs. 24.3±2.5, P = .002). Waist circumference was independently associated with the presence of SDB after adjustment for BMI, systolic blood pressure and fasting blood glucose in multiple regression analyses. Subjects with SDB had a higher prevalence of central obesity (72.5% vs. 42.5%, P = .002), hyperglycemia (45.0% vs. 26.3%, P = .04), MS (45.0% vs. 22.5%, P = .01) and number of MS components (2.4 ± 1.6 vs. 1.7 ± 1.4, P = .01) than the control group. Waist circumference was significantly correlated with both CPC-AHI (r = .492, P = .0013) and PSG-AHI (r = .699, P < .0001) in the SDB group. Conclusions: SDB was associated with a higher prevalence of MS and its individual components, notably central obesity, in a Chinese general population. Large-scale screening of high risk population with MS to identify subjects with SDB for appropriate management is warranted.