scholarly journals Entropy and Multifractal-Multiscale Indices of Heart Rate Time Series to Evaluate Intricate Cognitive-Autonomic Interactions

Entropy ◽  
2021 ◽  
Vol 23 (6) ◽  
pp. 663
Author(s):  
Pierre Bouny ◽  
Laurent M. Arsac ◽  
Emma Touré Touré Cuq ◽  
Veronique Deschodt-Arsac
Keyword(s):  
Heart Rate ◽  
Time Series ◽  
Spatial Scales ◽  
Selective Inhibition ◽  
Emergent Behavior ◽  
Multiscale Entropy ◽  
Fluctuation Analysis ◽  
Cognitive Interference ◽  
Sensorimotor Network ◽  
Multifractal Behavior

Recent research has clarified the existence of a networked system involving a cortical and subcortical circuitry regulating both cognition and cardiac autonomic control, which is dynamically organized as a function of cognitive demand. The main interactions span multiple temporal and spatial scales and are extensively governed by nonlinear processes. Hence, entropy and (multi)fractality in heart period time series are suitable to capture emergent behavior of the cognitive-autonomic network coordination. This study investigated how entropy and multifractal-multiscale analyses could depict specific cognitive-autonomic architectures reflected in the heart rate dynamics when students performed selective inhibition tasks. The participants () completed cognitive interference (Stroop color and word task), action cancellation (stop-signal) and action restraint (go/no-go) tasks, compared to watching a neutral movie as baseline. Entropy and fractal markers (respectively, the refined composite multiscale entropy and multifractal-multiscale detrended fluctuation analysis) outperformed other time-domain and frequency-domain markers of the heart rate variability in distinguishing cognitive tasks. Crucially, the entropy increased selectively during cognitive interference and the multifractality increased during action cancellation. An interpretative hypothesis is that cognitive interference elicited a greater richness in interactive processes that form the central autonomic network while action cancellation, which is achieved via biasing a sensorimotor network, could lead to a scale-specific heightening of multifractal behavior.

scholarly journals Multiscale Entropy of Cardiac and Postural Control Reflects a Flexible Adaptation to a Cognitive Task

Entropy ◽  
2019 ◽  
Vol 21 (10) ◽  
pp. 1024 ◽  
Author(s):  
Estelle Blons ◽  
Laurent Arsac ◽  
Pierre Gilfriche ◽  
Veronique Deschodt-Arsac
Keyword(s):  
Heart Rate ◽  
Time Series ◽  
Postural Sway ◽  
Cognitive Task ◽  
Temporal Structure ◽  
Well Being ◽  
Multiscale Entropy ◽  
Cognitive Tasks ◽  
High Entropy ◽  
Stable Conditions

In humans, physiological systems involved in maintaining stable conditions for health and well-being are complex, encompassing multiple interactions within and between system components. This complexity is mirrored in the temporal structure of the variability of output signals. Entropy has been recognized as a good marker of systems complexity, notably when calculated from heart rate and postural dynamics. A degraded entropy is generally associated with frailty, aging, impairments or diseases. In contrast, high entropy has been associated with the elevated capacity to adjust to an ever-changing environment, but the link is unknown between entropy and the capacity to cope with cognitive tasks in a healthy young to middle-aged population. Here, we addressed classic markers (time and frequency domains) and refined composite multiscale entropy (MSE) markers (after pre-processing) of heart rate and postural sway time series in 34 participants during quiet versus cognitive task conditions. Recordings lasted 10 min for heart rate and 51.2 s for upright standing, providing time series lengths of 500–600 and 2048 samples, respectively. The main finding was that entropy increased during cognitive tasks. This highlights the possible links between our entropy measures and the systems complexity that probably facilitates a control remodeling and a flexible adaptability in our healthy participants. We conclude that entropy is a reliable marker of neurophysiological complexity and adaptability in autonomic and somatic systems.

Abstract P422: Two-kidney One Clip (2k1c) Hypertension Reduces Complexity of Heart Rate Variability in Mice

Hypertension ◽  
2017 ◽  
Vol 70 (suppl_1) ◽  
Author(s):  
Fernanda L Rodrigues ◽  
Luiz E Silva ◽  
Carlos A Silva ◽  
Fernando S Carneiro ◽  
Rita C Tostes ◽  
...  
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Multiscale Entropy ◽  
Left Renal Artery ◽  
Fluctuation Analysis ◽  
Detrending Fluctuation Analysis ◽  
Interval Time Series ◽  
Electrocardiogram Ecg ◽  
2K1c Hypertension

Introduction: Hypertension is the most common chronic cardiovascular disease, being multifactorial in origin and an important cause of morbidity and mortality worldwide. Complex behaviors of heart rate series have been widely recognized and the loss of complexity in heart rate variability (HRV) has been shown to predict adverse cardiovascular outcomes. We hypothesized that two-kidney one clip (2K1C) hypertension reduces the HRV complexity in mice. Methods and Results: C57BL/6 mice were anesthetized with isoflurane and submitted to 2K1C hypertension by placing a silver clip (0.12 mm) around left renal artery. After 4 weeks, mice were implanted with subcutaneous electrocardiogram (ECG) electrodes and allowed to recover for 48 h. On the day of the experiment, the ECG was recorded for 30 minutes in conscious, unrestrained mice. At the end of the recording, arterial pressure (AP) was directly measured in each mouse under isoflurane anesthesia. RR interval time series were generated and the complexity of HRV was determined using detrending fluctuation analysis (DFA) and multiscale entropy (MSE). Mean AP was higher in 2K1C mice (133±2 vs 93±4 mmHg) while the HR was similar between groups. DFA scaling exponents were calculated in short (5 to 15), mid (30 to 200) and long (200 to 1500) window sizes, but only the long-term exponent was different between groups (1.27±0.09 vs 0.89±0.08 in 2K1C and sham mice, respectively). MSE was calculated up to scale 20 and averaged in short (1 to 5) and long (6 to 20) time scales. In both short (0.75±0.16 vs 1.25±0.11) and long (0.76±0.17 vs 1.22±0.09) ranges, entropy is lower in hypertensive mice. Conclusions: The complexity of HRV dynamics was found lower in renovascular hypertensive mice. Both sympathetic and vagal control of the heart seems to be involved in this process, as predictability (MSE) and fractality (DFA) is affected in various temporal scales. Nevertheless, the greatest entropy difference between groups is found at scale 6, which is closely related to respiration.

scholarly journals Nonlinear Methods Most Applied to Heart-Rate Time Series: A Review

Entropy ◽  
2020 ◽  
Vol 22 (3) ◽  
pp. 309 ◽  
Author(s):  
Teresa Henriques ◽  
Maria Ribeiro ◽  
Andreia Teixeira ◽  
Luísa Castro ◽  
Luís Antunes ◽  
...  
Keyword(s):  
Heart Rate ◽  
Recurrence Plot ◽  
Multiscale Entropy ◽  
Fluctuation Analysis ◽  
Mathematical Methods ◽  
Poincaré Plot ◽  
Nonlinear Methods ◽  
Plot Analysis ◽  
Heart Rate Dynamics ◽  
Recurrence Plot Analysis

The heart-rate dynamics are one of the most analyzed physiological interactions. Many mathematical methods were proposed to evaluate heart-rate variability. These methods have been successfully applied in research to expand knowledge concerning the cardiovascular dynamics in healthy as well as in pathological conditions. Notwithstanding, they are still far from clinical practice. In this paper, we aim to review the nonlinear methods most used to assess heart-rate dynamics. We focused on methods based on concepts of chaos, fractality, and complexity: Poincaré plot, recurrence plot analysis, fractal dimension (and the correlation dimension), detrended fluctuation analysis, Hurst exponent, Lyapunov exponent entropies (Shannon, conditional, approximate, sample entropy, and multiscale entropy), and symbolic dynamics. We present the description of the methods along with their most notable applications.

scholarly journals DISCRIMINATION BETWEEN HEALTHY AND SICK CARDIAC AUTONOMIC NERVOUS SYSTEM BY DETRENDED HEART RATE VARIABILITY ANALYSIS

Fractals ◽  
1999 ◽  
Vol 07 (01) ◽  
pp. 85-91 ◽  
Author(s):  
Y. ASHKENAZY ◽  
M. LEWKOWICZ ◽  
J. LEVITAN ◽  
S. HAVLIN ◽  
K. SAERMARK ◽  
...  
Keyword(s):  
Heart Rate ◽  
Time Series ◽  
Heart Rate Variability ◽  
Nervous System ◽  
Healthy Subjects ◽  
Heart Rate Variability Analysis ◽  
Fluctuation Analysis ◽  
Cardiac Autonomic Nervous System ◽  
Data Points ◽  
Short Time

Multiresolution Wavelet Transform and Detrended Fluctuation Analysis have recently been proven to be excellent methods in the analysis of Heart Rate Variability and in distinguishing between healthy subjects and patients with various dysfunctions of the cardiac nervous system. We argue that it is possible to obtain a distinction between healthy subjects/patients of at least similar quality by, first, detrending the time-series of RR-intervals by subtracting a running average based on a local window with a length of around 32 data points, then calculating the standard deviation of the detrended time-series. The results presented here indicate that the analysis can be based on very short time-series of RR-data (7–8 minutes), which is a considerable improvement relative to 24-hour Holter recordings.

scholarly journals State Change Probability: A Measure of the Complexity of Cardiac RR Interval Time Series Using Physiological State Change with Statistical Hypothesis Testing

2019 ◽  
Author(s):  
Hsuan-Hao Chao ◽  
Han-Ping Huang ◽  
Sung-Yang Wei ◽  
Chang Francis Hsu ◽  
Long Hsu ◽  
...  
Keyword(s):  
Heart Rate ◽  
Time Series ◽  
White Noise ◽  
Hypothesis Test ◽  
Statistical Tests ◽  
Physiological State ◽  
Statistical Hypothesis ◽  
Multiscale Entropy ◽  
State Change ◽  
State Changes

AbstractThe complexity of biological signals has been proposed to reflect the adaptability of a given biological system to different environments. Two measures of complexity—multiscale entropy (MSE) and entropy of entropy (EoE)—have been proposed, to evaluate the complexity of heart rate signals from different perspectives. The MSE evaluates the information content of a long time series across multiple temporal scales, while the EoE characterizes variation in amount of information, which is interpreted as the “state changing,” of segments in a time series. However, both are problematic when analyzing white noise and are sensitive to data size. Therefore, based on the concept of “state changing,” we propose state change probability (SCP) as a measure of complexity. SCP utilizes a statistical hypothesis test to determine the physiological state changes between two consecutive segments in heart rate signals. The SCP value is defined as the ratio of the number of state changes to total number of consecutive segment pairs. Two common statistical tests, the t-test and Wilcoxon rank–sum test, were separately used in the SCP algorithm for comparison, yielding similar results. The SCP method is capable of reasonably evaluating the complexity of white noise and other signals, including 1/f noise, periodic signals, and heart rate signals, from healthy subjects, as well as subjects with congestive heart failure or atrial fibrillation. The SCP method is also insensitive to data size. A universal SCP threshold value can be applied, to differentiate between healthy and pathological subjects for data sizes ranging from 100 to 10,000 points. The SCP algorithm is slightly better than the EoE method when differentiating between subjects, and is superior to the MSE method.

scholarly journals The role of sympathetic and vagal cardiac control on complexity of heart rate dynamics

2017 ◽  
Vol 312 (3) ◽  
pp. H469-H477 ◽  
Author(s):  
Luiz Eduardo Virgilio Silva ◽  
Carlos Alberto Aguiar Silva ◽  
Helio Cesar Salgado ◽  
Rubens Fazan
Keyword(s):  
Heart Rate ◽  
Detrended Fluctuation Analysis ◽  
Surrogate Data ◽  
Diagnostic Methods ◽  
Multiscale Entropy ◽  
Fluctuation Analysis ◽  
Maximal Effect ◽  
Future Studies ◽  
Selective Blockade ◽  
Detrended Fluctuation

Analysis of heart rate variability (HRV) by nonlinear approaches has been gaining interest due to their ability to extract additional information from heart rate (HR) dynamics that are not detectable by traditional approaches. Nevertheless, the physiological interpretation of nonlinear approaches remains unclear. Therefore, we propose long-term (60 min) protocols involving selective blockade of cardiac autonomic receptors to investigate the contribution of sympathetic and parasympathetic function upon nonlinear dynamics of HRV. Conscious male Wistar rats had their electrocardiogram (ECG) recorded under three distinct conditions: basal, selective (atenolol or atropine), or combined (atenolol plus atropine) pharmacological blockade of autonomic muscarinic or β1-adrenergic receptors. Time series of RR interval were assessed by multiscale entropy (MSE) and detrended fluctuation analysis (DFA). Entropy over short (1 to 5, MSE1–5) and long (6 to 30, MSE6–30) time scales was computed, as well as DFA scaling exponents at short (αshort, 5 ≤ n ≤ 15), mid (αmid, 30 ≤ n ≤ 200), and long (αlong, 200 ≤ n ≤ 1,700) window sizes. The results show that MSE1–5 is reduced under atropine blockade and MSE6–30 is reduced under atropine, atenolol, or combined blockade. In addition, while atropine expressed its maximal effect at scale six, the effect of atenolol on MSE increased with scale. For DFA, αshort decreased during atenolol blockade, while the αmid increased under atropine blockade. Double blockade decreased αshort and increased αlong. Results with surrogate data show that the dynamics during combined blockade is not random. In summary, sympathetic and vagal control differently affect entropy (MSE) and fractal properties (DFA) of HRV. These findings are important to guide future studies. NEW & NOTEWORTHY Although multiscale entropy (MSE) and detrended fluctuation analysis (DFA) are recognizably useful prognostic/diagnostic methods, their physiological interpretation remains unclear. The present study clarifies the effect of the cardiac autonomic control on MSE and DFA, assessed during long periods (1 h). These findings are important to help the interpretation of future studies.

scholarly journals Multiscale Distribution Entropy Analysis of Short-Term Heart Rate Variability

Entropy ◽  
2018 ◽  
Vol 20 (12) ◽  
pp. 952 ◽  
Author(s):  
Dae-Young Lee ◽  
Young-Seok Choi
Keyword(s):  
Heart Rate ◽  
Time Series ◽  
Heart Rate Variability ◽  
Coarse Grained ◽  
Multiscale Entropy ◽  
Short Term ◽  
Ecg Signals ◽  
Improved Stability ◽  
Moving Averaging ◽  
Electrocardiogram Ecg

Electrocardiogram (ECG) signal has been commonly used to analyze the complexity of heart rate variability (HRV). For this, various entropy methods have been considerably of interest. The multiscale entropy (MSE) method, which makes use of the sample entropy (SampEn) calculation of coarse-grained time series, has attracted attention for analysis of HRV. However, the SampEn computation may fail to be defined when the length of a time series is not enough long. Recently, distribution entropy (DistEn) with improved stability for a short-term time series has been proposed. Here, we propose a novel multiscale DistEn (MDE) for analysis of the complexity of short-term HRV by utilizing a moving-averaging multiscale process and the DistEn computation of each moving-averaged time series. Thus, it provides an improved stability of entropy evaluation for short-term HRV extracted from ECG. To verify the performance of MDE, we employ the analysis of synthetic signals and confirm the superiority of MDE over MSE. Then, we evaluate the complexity of short-term HRV extracted from ECG signals of congestive heart failure (CHF) patients and healthy subjects. The experimental results exhibit that MDE is capable of quantifying the decreased complexity of HRV with aging and CHF disease with short-term HRV time series.

scholarly journals Multifractal Characterization of Seismic Activity in the Provinces of Esmeraldas and Manabí, Ecuador

Proceedings ◽  
2019 ◽  
Vol 24 (1) ◽  
pp. 27 ◽  
Author(s):  
David I. Cuenca ◽  
Javier Estévez ◽  
Amanda P. García-Marín
Keyword(s):  
Time Series ◽  
Seismic Activity ◽  
Research Work ◽  
Fluctuation Analysis ◽  
Multifractal Detrended Fluctuation Analysis ◽  
Interevent Time ◽  
Large Fluctuations ◽  
Multifractal Behavior ◽  
The Right

Due to the enormous impact of seismic activity and the need to deepen knowledge of its behavior, this research work carries out an analysis of the multifractal nature of the magnitude, inter-distance and interevent time series of earthquakes that occurred in Ecuador during the years 2011–2017 in the provinces of Manabí and Esmeraldas, two areas with high seismic activity. For this study we use multifractal detrended fluctuation analysis (MF-DFA), which allows the detection of multifractality in a non-stationary series as well as in a series of parameters of non-linear characterization. The obtained results revealed that an interevent time series presents a higher degree of multifractality than the two previously mentioned. In addition, the Hurst exponent values were in a non-proportional function to (q), which is a weight value indicating the multifractal behavior of the dynamics of the earthquakes analyzed in this work. Finally, several multifractal parameters were calculated, and as a result all series were skewed to the right. This reveals that small variations in the analyzed series were more dominant than large fluctuations.

scholarly journals Autonomic dysfunction characterized by Heart Rate Variability among patients with Sepsis-related Acute Respiratory Failure

2021 ◽  
Author(s):  
Preethi Krishnan ◽  
Curtis Marshall ◽  
Philip Yang ◽  
Sivasubramanium V Bhavani ◽  
Andre Holder ◽  
...  
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Respiratory Failure ◽  
Acute Respiratory Failure ◽  
Time Domain ◽  
Statistical Significance ◽  
Low Frequency ◽  
Multiscale Entropy ◽  
Physiological Data ◽  
Fluctuation Analysis

Abstract Rationale: To explore the association and implications of using Heart rate variability (HRV) derived from continuous bedside monitoring as a surrogate for detection of Acute Respiratory Failure (ARF) in critically ill sepsis patients. Objective: To analyze HRV measures derived from continuous physiological data captured before ARF-onset to determine whether statistically significant markers can be characterized when compared to sepsis controls. Methods: Retrospective HRV analysis of sepsis patients admitted to Emory Healthcare ICUs was performed between ARF and age and gender-matched controls. HRV measures such as time domain, frequency domain, nonlinear, and complexity measures were analyzed up to 1 hour before the onset of ARF, and a random event time in the sepsis-controls. Statistical significance was computed by the Wilcoxon Rank Sum test. Results: A total of 89 intensive care unit (ICU) patients with sepsis were included in this retrospective cohort study. Time-domain HRV measures including pNN50 (the fraction of consecutive NN intervals that differ by more than 50 ms), RMSSD (root-mean-square differences of successive NN intervals), standard deviation, interquartile range, variance, and approximate entropy for Beat-to-Beat intervals strongly distinguished ARF patients from the controls group. HRV measures for nonlinear and frequency domains were significantly altered (p<0.05) among sepsis patients with ARF compared to controls. Frequency measures such as low frequency (LF), very low frequency (VLF), high frequency (HF), and SD1/SD2 ratio nonlinear measure (SD1:SD2) also showed a significant (p<0.05) increase in the ARF group patients. Multiscale entropy complexity was lower for ARF patients compared to the control counterparts. Detrended fluctuation analysis (DFA) showed a decreasing trend in ARF patients. Conclusions: HRV was significantly impaired across sepsis patients who developed ARF when compared to sepsis controls, indicating a potential prognostic utility for earlier identification of the need for mechanical ventilation and management of patients suspected with sepsis.

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