Short-term Heart Rate Variability Signals Research Articles

Renyi Distribution Entropy Analysis of Short-Term Heart Rate Variability Signals and Its Application in Coronary Artery Disease Detection.

Coronary artery disease (CAD) is a life-threatening condition that, unless treated at an early stage, can lead to congestive heart failure, ischemic heart disease, and myocardial infarction. Early detection of diagnostic features underlying electrocardiography signals is crucial for the identification and treatment of CAD. In the present work, we proposed novel entropy called Renyi Distribution Entropy (RdisEn) for the analysis of short-term heart rate variability (HRV) signals and the detection of CAD. Our simulation experiment with synthetic, physiological, and pathological signals demonstrated that RdisEn could distinguish effectively among different subject groups. Compared to the values of sample entropy or approximation entropy, the RdisEn value was less affected by the parameter choice, and it remained stable even in short-term HRV. We have developed a combined CAD detection scheme with RdisEn and wavelet packet decomposition (WPD): (1) Normal and CAD HRV beats obtained were divided into two equal parts. (2) Feature acquisition: RdisEn and WPD-based statistical features were calculated from one part of HRV beats, and student’s t-test was performed to select clinically significant features. (3) Classification: selected features were computed from the remaining part of HRV beats and fed into K-nearest neighbor and support vector machine, to separate CAD from normal subjects. The proposed scheme automatically detected CAD with 97.5% accuracy, 100% sensitivity and 95% specificity and performed better than most of the existing schemes.

Understanding Irregularity Characteristics of Short-Term HRV Signals Using Sample Entropy Profile.

Sample entropy (), a popularly used "regularity analysis" tool, has restrictions in handling short-term segments (largely ) of heart rate variability (HRV) data. For such short signals, the estimate either remains undefined or fails to retrieve "accurate" regularity information. These limitations arise due to the extreme dependence of on its functional parameters, in particular the tolerance . Evaluating at a single random choice of parameter is a major cause of concern in being able to extract reliable and complete regularity information from a given signal. Here, we hypothesize that, finding a complete profile of (in contrast to a single estimate) corresponding to a data specific set of values may facilitate enhanced information retrieval from short-term signals. We introduce a novel and computationally efficient concept of profiling in order to eliminate existing inaccuracies seen in the case of estimation. Using three different HRV datasets from the PhysioNet database-first, real and simulated, second, elderly and young, and third, healthy and arrhythmic; we demonstrate better definiteness and classification performance of profile based estimates ( and ) when compared to conventional and estimates. Our novelty is to identify the importance of reliability in short-term signal regularity analysis, and our proposed approach aims to enhance both quality and quantity of information from any short-term signal.

Use of Accumulated Entropies for Automated Detection of Congestive Heart Failure in Flexible Analytic Wavelet Transform Framework Based on Short-Term HRV Signals

In the present work, an automated method to diagnose Congestive Heart Failure (CHF) using Heart Rate Variability (HRV) signals is proposed. This method is based on Flexible Analytic Wavelet Transform (FAWT), which decomposes the HRV signals into different sub-band signals. Further, Accumulated Fuzzy Entropy (AFEnt) and Accumulated Permutation Entropy (APEnt) are computed over cumulative sums of these sub-band signals. This provides complexity analysis using fuzzy and permutation entropies at different frequency scales. We have extracted 20 features from these signals obtained at different frequency scales of HRV signals. The Bhattacharyya ranking method is used to rank the extracted features from the HRV signals of three different lengths (500, 1000 and 2000 samples). These ranked features are fed to the Least Squares Support Vector Machine (LS-SVM) classifier. Our proposed system has obtained a sensitivity of 98.07%, specificity of 98.33% and accuracy of 98.21% for the 500-sample length of HRV signals. Our system yielded a sensitivity of 97.95%, specificity of 98.07% and accuracy of 98.01% for HRV signals of a length of 1000 samples and a sensitivity of 97.76%, specificity of 97.67% and accuracy of 97.71% for signals corresponding to the 2000-sample length of HRV signals. Our automated system can aid clinicians in the accurate detection of CHF using HRV signals. It can be installed in hospitals, polyclinics and remote villages where there is no access to cardiologists.

Comparison of Machine Learning Methods for the Arterial Hypertension Diagnostics.

The paper presents results of machine learning approach accuracy applied analysis of cardiac activity. The study evaluates the diagnostics possibilities of the arterial hypertension by means of the short-term heart rate variability signals. Two groups were studied: 30 relatively healthy volunteers and 40 patients suffering from the arterial hypertension of II-III degree. The following machine learning approaches were studied: linear and quadratic discriminant analysis, k-nearest neighbors, support vector machine with radial basis, decision trees, and naive Bayes classifier. Moreover, in the study, different methods of feature extraction are analyzed: statistical, spectral, wavelet, and multifractal. All in all, 53 features were investigated. Investigation results show that discriminant analysis achieves the highest classification accuracy. The suggested approach of noncorrelated feature set search achieved higher results than data set based on the principal components.

DETECTION OF HUMAN STRESS USING SHORT-TERM ECG AND HRV SIGNALS

This paper introduces a method for resolving the problem of human stress detection through short-term (less than 5 min) electrocardiogram (ECG) and heart rate variability (HRV) signals. The explored methodology helps to improve the stress detection rate and reliability through multiple evidences originated in same sensor. In this work, stress-inducing protocol, data acquisition, preprocessing, feature extraction and classification are the major steps involved to detect the stress. In total, 60 subjects (30 males and 30 females) participated in the Stroop color word-based stress-inducing task and ECG signal was acquired simultaneously. The wavelet denoising algorithm was applied to remove high frequency, baseline wander and power line noises. Discrete wavelet transform (DWT)-based heart rate (HR) detection algorithm is used for deriving HRV signal from the preprocessed ECG signal. The ectopic beat removal method is employed to eliminate the ectopic beat and noise peaks in the HRV signal. In order to detect the stress, the issue of uneven sampling with the HRV signal has been successfully rectified using the Lomb-Scargle periodogram (LSP). The application of LSP in short-term HRV signals (32 s), uneven sampling issue, and power spectral information issue has been rectified and the trustworthiness of the short-term HRV signal has been proved by hypothesis as well as experimental results. Theoretical analysis suggested that a minimum 25 s of online or offline ECG data is required to analyze the autonomous nervous system (ANS) activity related to stress. In addition to the HRV signal, ECG-based stress assessment has been proposed to detect the stress through optimum features using fast Fourier transform (FFT). Various features extracted from the ECG and HRV signal have been classified into normal and stress using PNN and kNN classifiers with different smoothing factor and k values. The experimental results indicate that the proposed methodology for short-term ECG and HRV signal can achieve the overall average classification accuracy of 91.66% and 94.66% in the subject-independent mode.

Sign series entropy analysis of short-term heart rate variability

Complexity and nonlinearity approaches can be used to study the temporal and structural order in heart rate variability (HRV) signal, which is helpful for understanding the underlying rule and physiological essence of cardiovascular regulation. For clinical applications, methods suitable for short-term HRV analysis are more valuable. In this paper, sign series entropy analysis (SSEA) is proposed to characterize the feature of direction variation of HRV. The results show that SSEA method can detect sensitively physiological and pathological changes from short-term HRV signals, and the method also shows its robustness to nonstationarity and noise. Thus, it is suggested as an efficient way for the analysis of clinical HRV and other complex physiological signals.

Alteration in scaling behavior of short-term heartbeat time series for professional shooting athletes from rest to exercise

Scaling analysis of heartbeat time series has emerged as a useful tool for assessing the autonomic cardiac control under various physiologic and pathologic conditions. We study the heartbeat activity and scaling behavior of heartbeat fluctuations regulated by autonomic nervous system for professional shooting athletes under two states: rest and exercise, by applying the detrended fluctuation analysis method. We focus on alteration in correlation properties of heartbeat intervals for the shooters from rest to exercise, which may have a potential value in monitoring the quality of training and evaluating the sports capacity of the athletes. The result shows that scaling exponents of short-term heart rate variability signals from the shooters get significantly larger during exercise compared with those obtained at rest. It demonstrates that during exercise stronger correlations appear in the heartbeat series of shooting athletes in order to satisfy the specific requirements for high concentration and better control on their heart beats.

Agreement of two entropy-based measures on quantifying the complexity of short-term heart rate variability signals from professional shooters

Using two entropy-based measures, namely the approximate entropy and sample entropy measures, we studied the complexity of heart rate variability signals obtained from professional shooting athletes in the situations of rest and practice match. The results demonstrate that the values of two measures calculated from the resting signals are both greater than those calculated from the training signals, which means that the signals collected during the match are more regular compared to those acquired in a resting state. For a better application of the two methods, we further investigated the influences of two factors: threshold r and data length N, on the performance of the algorithms. Although both approaches have the ability to discriminate the complexity of heart beat interval series from different states of the shooters, provided that the parameters required by the algorithms are chosen within a proper range, it still seems that sample entropy method is more appropriate in quantifying the short-term heart rate variability signals for shooting athletes, especially when the time series are only several hundred points long.

The base-scale entropy analy-sis of short-term heart rate variability signal

The complexity of heart rate variability (HRV) signal can reflect physiological functions and healthy status of heart system. Detecting complexity of the short-term HRV signal has an important practical meaning. We introduce the base-scale entropy method to analyze the complexity of time series. The advantages of our method are its simplicity, extremely fast calculation for very short data and anti-noise characteristic. For the well-known chaotic dynamical system—logistic map, it is shown that our complexity behaves similarly to Lyapunov exponents, and is especially effective in the presence of random Gaussian noise. This paper addresses the use of base-scale entropy method to 3 low-dimensional nonlinear deterministic systems. At last, we apply this idea to short-term HRV signal, and the result shows the method could robustly identify patterns generated from healthy and pathologic states, as well as aging. The base-scale entropy can provide convenience in practically applications.

Nonlinearity degree of short-term heart rate variability signal

A nonlinear autoregressive (NAR) model is built to model the heartbeat interval time series and the optimum model degree is proposed to be taken to evaluate the nonlinearity degree of heart rate variability (HRV). A group of healthy persons are studied and the results indicate that this method can effectively get nonlinear information from short (6–7 min) heartbeat series and consequently reflect the degree of heart rate variability, which supplies convenience in clinical application. Finally, a comparison with the traditional time domain method shows that the NAR model method can reflect the complexity of the whole signal and lessen the influence of noise and instability in the signal.

Nonlinearity degree of short-term heart rate variability signal

Nonlinearity degree of short-term heart rate variability signal

Nonlinearity degree of short-term heart rate variability signal

Nonlinearity degree of short-term heart rate variability signal

Validity Test for a Set of Nonlinear Measures for Short Data Length with Reference to Short-Term Heart Rate Variability Signal

In this paper we investigate the requirementsfor the size of data set to be analyzed by a set of nonlinearmeasures. Time series from some standard nonlinear systems possessingchaotic behavior, as well as sinusoidal and random signals areconsidered. For Henon, Kaplan-Yorke and logistic mapswe found the measures of correlation dimension (CD), approximateentropy (AE), Lyapunov exponents (LE), and deterministic ratio(DR) to be reliable for data length as short as 500 samples.On short-term heart rate variability (HRV) signal, AE and DRmeasures were able to distinguish between various experimentallymodified states of autonomic nervous system (ANS) controllingthe heart rate (HR). Thus, titling and parasympathetic blockademake the system more deterministic and reduce its entropy whilstsympathetic blockade makes it less deterministic.