The Prognostic Value of Non-Linear Analysis of Heart Rate Variability in Patients with Congestive Heart Failure—A Pilot Study of Multiscale Entropy

Ho, Yi‐Lwun; Lin, Chen; Lin, Yu Hung; Lo, Men Tzung

doi:10.1371/journal.pone.0018699

Cited by 94 publications

(121 citation statements)

References 34 publications

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“…Before performing MSE, we employed empirical mode decomposition (EMD) [19] to remove trend (last component of EMD) from the epoched EEG data. This detrend process by EMD has been demonstrated as efficient to improve the stationarity of the signals [20][21][22][23]. For stop trials, the MSE analysis was performed from scale 1 to 30 (the resolution of EEG signals is 1 ms) in two time windows: (1) −750~−300 ms; and (2) −300~150 ms relative to the stop signal onset.…”

Section: Mse Analysismentioning

confidence: 99%

Dynamical Change of Signal Complexity in the Brain During Inhibitory Control Processes

Huang

Tseng²,

Liang

2015

Entropy

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Abstract:The ability to inhibit impulses and withdraw certain responses are essential for human's survival in a fast-changing environment. These processes happen fast, in a complex manner, and require our brain to make a fast adaptation to inhibit the impulsive response. The present study employs multiscale entropy (MSE) to analyzing electroencephalography (EEG) signals acquired alongside a behavioral stop-signal task to theoretically quantify the complexity (indicating adaptability and efficiency) of neural systems to investigate the dynamical change of complexity in the brain during the processes of inhibitory control. We found that the complexity of EEG signals was higher for successful than unsuccessful inhibition in the stage of peri-stimulus, but not in the pre-stimulus time window. In addition, we found that the dynamical change in the brain from pre-stimulus to peri-stimulus stage for inhibitory control is a process of decreasing complexity. We demonstrated both by sensor-level and source-level MSE that the processes of losing complexity is temporally slower and spatially restricted for successful inhibition, and is temporally quicker and spatially extensive for unsuccessful inhibition.

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Section: Mse Analysismentioning

confidence: 99%

Dynamical Change of Signal Complexity in the Brain During Inhibitory Control Processes

Huang

Tseng²,

Liang

2015

Entropy

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“…Nonlinear measures encompass a heterogeneous group of statistics, several of which quantify the entropy or complexity of the signal. Multiscale sample entropy is one such statistic that has been used to distinguish patients with cardiovascular pathologies from healthy subjects (10,29,30) and is predictive of mortality in trauma and heart failure patients (17,21,22,26).…”

mentioning

confidence: 99%

Validation of pulse rate variability as a surrogate for heart rate variability in chronically instrumented rabbits

Pellegrino

Schiller

Zucker

2014

American Journal of Physiology-Heart and Circulatory Physiology

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Pellegrino PR, Schiller AM, Zucker IH. Validation of pulse rate variability as a surrogate for heart rate variability in chronically instrumented rabbits. Am J Physiol Heart Circ Physiol 307: H97-H109, 2014. First published May 2, 2014; doi:10.1152/ajpheart.00898.2013.-Heart rate variability (HRV) is a function of cardiac autonomic tone that is widely used in both clinical and animal studies. In preclinical studies, HRV measures are frequently derived using the arterial pulse waveform from an implanted pressure telemetry device, termed pulse rate variability (PRV), instead of the electrocardiogram signal in accordance with clinical guidelines. The acceptability of PRV as a surrogate for HRV in instrumented animals is unknown. Using rabbits implanted with intracardiac leads and chronically implanted pressure transducers, we investigated the correlation and agreement of timedomain, frequency-domain, and nonlinear indexes of HRV and PRV at baseline. We also investigated the effects of ventricular pacing and autonomic blockade on both measures. At baseline, HRV and PRV time-and frequency-domain parameters showed robust correlations and moderate to high agreement, whereas nonlinear parameters showed slightly weaker correlations and varied agreement. Ventricular pacing almost completely eliminated HRV, and spectral analysis of the PRV signal revealed a HRV-independent rhythm. After cardiac autonomic blockade with atropine or metoprolol, the changes in timeand non-normalized frequency-domain measures of PRV continued to show strong correlations and moderate to high agreement with corresponding changes in HRV measures. Blockade-induced changes in nonlinear PRV indexes correlated poorly with HRV changes and showed weak agreement. These results suggest that time-and frequency-domain measures of PRV are acceptable surrogates for HRV even in the context of changing cardiac autonomic tone, but caution should be used when nonlinear measures are a primary end point or when HRV is very low as HRV-independent rhythms may predominate.heart rate variability; pulse rate variability; pulse interval; sympathovagal balance

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“…Recently, heart-rate dynamics have been examined by (13) using detrended fluctuation analysis (DFA) to examine the crossover changes phenomenon of the fractal correlation exponents between short and long time scales. The short-term exponent is understood to be examined using cardiorespiratory interaction (13,14). Multiscale entropy (MSE) analysis, proposed by a number of previous studies (15)(16)(17), using entropy based methods, is able to measure the complexity of nonlinear signals at multiple temporal scales.…”

Section: Introductionmentioning

confidence: 99%

Multiscaled Complexity Analysis of EEG Epileptic Seizure Using Entropy-Based Techniques

et al. 2018

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Objectives: The most common chronic disorder due to sudden change in the electrical activity of the brain is known as epilepsy. It causes millions of deaths every year and is the second major disorder after stroke. The epileptic process involves an abnormal synchronized firing of neurons usually characterized by recurrent seizures, which are highly complex, nonlinear and non-stationary in nature. Even between seizures, the epileptic brain is different from normal and pathological conditions. The classical methods fail to analyse the full dynamics in detecting epileptic seizure. The aim of this research was to quantify the dynamics of EEG signals between seizures and seizure-free intervals using entropy based complexity measures at multiple temporal scales. The complexity of epileptic seizure intervals is reduced because of degradation of structural and functional components. Thus, complexity measures are more robust to fully analyse the dynamics of these signals. Methods:The publicly available data comprise of three different groups of EEG signals: 1, Healthy subjects; 2, Epileptic subjects during seizure-free intervals (interictal EEG); and 3, Epileptic subjects during seizure (ictal EEG); each of 100 EEG channel sample at 174 Hz were taken to quantify the dynamics in these signals. To analyse the improved understanding of the epileptic process, complexity-based techniques of Multiscale Sample Entropy (MSE) and Wavelet Entropy (Wentropy) including Shannon, log energy, and threshold, and sure and norm developed in Matlab 2015a, were employed to distinguish these conditions. Mann-WhitneyWilcoxon (MWW) test was used to find significant differences among various groups at 0.05 significance level. Moreover, the area under the curve (AUC) was computed by developing multi-receiver operating curve (ROC) in Matlab 2015a to find the maximum separation to distinguish these conditions. Results: The complexity of healthy and epileptic subjects (including both in the presence of seizures and without seizure) was computed using MSE and Wentropy at multiple temporal scales. The healthy subjects exhibited higher complexity than the epileptic subjects. Likewise, the complexity of ictal (seizure subjects) was higher than the interictal (without seizures). To distinguish healthy subjects (Set O) from epileptic (Set S) subjects, the highest separation was obtained using wavelet norm 1.1 (AUC = 0.999) followed by wavelet Shannon (AUC = 0.9944), MSE (AUC = 0.9727) and wavelet threshold (AUC = 0.942). Conclusions: Optimal results using MSE were obtained at smaller scales whereas the wavelet entropies gave optimal results mostly at higher temporal scales. Moreover, the highest separation in form of AUC was obtained using the Wentropy method with norm parameter 1.1 to distinguish healthy (eye open) and epileptic seizure (ictal state) subjects followed by wavelet Shannon and MSE.

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The Prognostic Value of Non-Linear Analysis of Heart Rate Variability in Patients with Congestive Heart Failure—A Pilot Study of Multiscale Entropy

Cited by 94 publications

References 34 publications

Dynamical Change of Signal Complexity in the Brain During Inhibitory Control Processes

Dynamical Change of Signal Complexity in the Brain During Inhibitory Control Processes

Validation of pulse rate variability as a surrogate for heart rate variability in chronically instrumented rabbits

Multiscaled Complexity Analysis of EEG Epileptic Seizure Using Entropy-Based Techniques

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