scholarly journals Dominant Lyapunov exponent and approximate entropy in heart rate variability during emotional visual elicitation

2012 ◽  
Vol 5 ◽  
Author(s):  
Gaetano Valenza ◽  
Paolo Allegrini ◽  
Antonio Lanatà ◽  
Enzo Pasquale Scilingo
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Lyapunov Exponent ◽  
Approximate Entropy

scholarly journals Heart Rate Variability Assessment by the Lyapunov Exponent

2020 ◽  
Vol 9 (8) ◽  
pp. 724-728
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Lyapunov Exponent ◽  
Low Frequency ◽  
Approximate Entropy ◽  
Mean Value ◽  
Spectral Estimates ◽  
Power Spectral ◽  
Hrv Analysis ◽  
Entropy Estimates

Heart rate variability (HRV) is a measure that evaluates cardiac autonomic activity according to the complexity or irregularity of an HRV dataset. At present, among various entropy estimates, the Lyapunov exponent (LE) is not as well described as approximate entropy (ApEn) and sample entropy (SampEn). Therefore, in this study, we investigated the characteristics of the parameters associated with the LE to evaluate whether the LE parameters can replace the frequency-domain parameters for HRV analysis. For the LE analysis in this study, two-dimensional factors were adjusted: length, which determines the size of the dimension vectors and is known as time delay embedding, varied over a range of 1 to 7, and the interval, which determines the distance between two successive embedding vectors, varied over a range of 1 to 3. A new parameter similar to the LA, the accumulation of the LE, was developed along with the LE to characterize the HRV parameters. The high frequency (HF) components dominated when the mean value of the LA was largest for interval 2, with 2.89 ms2 at the low frequency (LF) and 4.32 ms2 at the HF. The root mean square of the successive difference (RMSSD) in the LE decreased with increasing length in interval 1 from 2.6056 for length 1 to 0.2666 for length 7, resulting in a low HRV. The results suggest that the Lyapunov exponent methodology could be used in characterizing HRV analysis and replace power spectral estimates, specifically, HF components.

scholarly journals Heart Rate Variability Assessment by the Entropy Parameters during Sleep

2020 ◽  
Vol 9 (9) ◽  
pp. 468-472
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Eye Movement ◽  
Discrimination Performance ◽  
Conditional Entropy ◽  
Approximate Entropy ◽  
Mean Value ◽  
Sleep Stages ◽  
The Mean

The nonlinear heart rate variability (HRV) parameter quantifies autonomic nervous system (ANS) activity based on the complexity or irregularity of an HRV dataset. At present, among various entropy-related parameters during sleep, approximate entropy (ApEn) and sample entropy (SampEn) are not as well understood as other entropy parameters such as Shannon entropy (SE) and conditional entropy (CE). Therefore, in this study, we investigated the characteristics of ApEn and SampEn to differentiate a rapid eye movement (REM) and nonrapid eye movement (NREM) for sleep stages. For nonlinear sleep HRV analysis, two target 10-minute, long-term HRV segments were obtained from each REM and NREM for 16 individual subjects. The target HRV segment was analyzed by moving the 2-minute window forward by 2 s, resulting in 240 results of each ApEn and SampEn. The ApEn and SampEn were averaged to obtain the mean value and standard deviation (SD) of all the results. SampEn provides excellent discrimination performance between REM and NREM in terms of the mean and SD (p<0.0001 and p=0.1989, respectively; 95% CI), but ApEn was inferior to SampEn (p=0.1980 and p=0.9931). The results indicate that SampEn, but not ApEn could be used to discriminate REM from NREM and detect various sleep-related incidents.

Characterization of Heart Rate Variability Changes Following Asphyxia in Rats

2004 ◽  
Vol 43 (01) ◽  
pp. 118-121 ◽  
Author(s):  
L. Moraru ◽  
L. Cimponeriu ◽  
S. Tong ◽  
N. Thakor ◽  
A. Bezerianos
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Nervous System ◽  
Cardiac Arrest ◽  
Frequency Domain ◽  
Linear Time ◽  
Approximate Entropy ◽  
Recovery Process ◽  
Autonomous Nervous System ◽  
Nonlinear Parameters

Summary Objectives: A non-invasive method to monitor the functioning of the autonomous nervous system consists in heart rate variability (HRV) analysis. The aim of this study was to investigate the changes on HRV after an asphyxia experiment in rats, using several linear (time and frequency domain) and nonlinear parameters (approximate entropy, SD1 and SD2 indices derived from Poincare plots). Methods: The experiments involved the study of HRV changes after cardiac arrest (CA) resulting from 5 min of hypoxia and asphyxia, followed by manual resuscitation and return of spontaneous circulation. 5 min stationary periods of RR intervals were selected for further analysis from 5 rats in following distinct situations: 1) baseline, 2) 30 min after CA, 3) 60 min after CA, 4) 90 min after CA, 5) 120 min after CA, 6) 150 min after CA. The ANS contribution has been delineated based on time and frequency domain analysis. Results and Conclusions: The results indicate that the recovery process following the asphyxia cardiac arrest reflects the impaired functioning of the autonomic nervous system. Both linear and nonlinear parameters track the different phases of the experiment, with an increased sensitivity displayed by the approximate entropy (ApEn). After 150 min the ApEn RRI parameter recovers to its baseline value. The results forward the ApEn as a more sensitive parameter of the recovery process following the asphyxia.

NONLINEAR HEART RATE VARIABILITY-BASED ANALYSIS AND PREDICTION OF PERFORMANCE STATUS IN PULMONARY METASTASES PATIENTS

2018 ◽  
Vol 30 (06) ◽  
pp. 1850043
Author(s):  
Reema Shyamsunder Shukla ◽  
Yogender Aggarwal
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Pulmonary Metastases ◽  
Performance Status ◽  
Eastern Cooperative Oncology Group ◽  
Approximate Entropy ◽  
Support Vector ◽  
Fluctuation Analysis ◽  
Nonlinear Features ◽  
Hrv Analysis

Cancer causes chronic stress and is associated with impaired autonomic nervous system (ANS). Heart rate variability (HRV) has been suggested to be an important tool in the identification and prediction of performance status (PS) in cancer. Lead II surface electrocardiogram (ECG) was recorded from 24 pulmonary metastases (PM) subjects and 30 healthy controls for nonlinear HRV analysis. Artificial neural network (ANN) and support vector machine (SVM) were applied for the prediction analysis. Analysis of variance (ANOVA) along with post-hoc Tukey’s HSD test was conducted using statistical R, 64-bit, v.3.3.2, at [Formula: see text]. The obtained results suggested lower HRV that increases with cancer severity from the Eastern Cooperative Oncology Group (ECOG)1 PS to ECOG4 PS. ANOVA results stated that approximate entropy (ApEn) ([Formula: see text]-[Formula: see text], [Formula: see text]), detrended fluctuation analysis (DFA) [Formula: see text] ([Formula: see text]-[Formula: see text], [Formula: see text]) and correlation dimension (CD) ([Formula: see text]-[Formula: see text], [Formula: see text]) were significant. The 13 nonlinear features were fed to ANN and SVM to obtain 82.25% and 100% accuracies, respectively. Nonlinear HRV analysis has given promising results in the prediction of diagnosis of PS in PM patients. These inputs would be very useful for clinicians to diagnose PS in their cancer patients and improve their quality of living.

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