Novel Classification of Coronary Artery Disease Using Heart Rate Variability Analysis

Abstract

Coronary artery disease (CAD) is a leading cause of death worldwide. Heart rate variability (HRV) has been proven to be a non-invasive marker of the autonomic modulation of the heart. Nonlinear analyses of HRV signals have shown that the HRV is reduced significantly in patients with CAD. Therefore, in this work, we extracted nonlinear features from the HRV signals using the following techniques: recurrence plots (RP), Poincare plots, and detrended fluctuation analysis (DFA). We also extracted three types of entropy, namely, Shannon entropy (ShanEn), approximation entropy (ApEn), and sample entropy (SampEn). These features were subjected to principal component analysis (PCA). The significant principal components were evaluated using eight classification techniques, and the performances of these techniques were evaluated to determine which presented the highest accuracy in classifying normal and CAD classes. We observed that the multilayer perceptron (MLP) method resulted in the highest classification accuracy (89.5%) using our proposed technique.

Automated diagnosis of coronary artery disease using tunable-Q wavelet transform applied on heart rate signals

“…Their work obtained an accuracy of 79.71% using 18 principal components. In [77], the nonlinear features from the heart rate signals are extracted using recurrence plots, Poincare plots, DFA, Shannon entropy, approximation entropy, and sample entropy to automatically detect CAD patients. These features after PCA have yielded a classification accuracy of 89.5% using multilayer perceptron (MLP) classifier using eight principal components.…”

Measuring Electromechanical Coupling in Patients with Coronary Artery Disease and Healthy Subjects

“…Patidar et al presented a new method for diagnosis of CAD using tunable-Q wavelet transform based features extracted from heart rate signals [49]. Many linear and nonlinear parameters are extracted from heart rate signals and used as diagnostic features to predict the subjects with CAD [3][4][5][6], the features were then fed into classifiers for automated diagnosis of CAD subjects [4][5][6]. In our previous publication, we have reported the univariate analysis results for RRI and DTI (analysis of RRI is also performed in this study, the results are shown in Table S1 in supplementary materials), using sample entropy, fuzzy entropy, and refined fuzzy entropy.…”

“…Researchers have studied the linear and nonlinear features of HRV for different positions (e.g., the supine, left lateral and right lateral positions), and the features were fed into classifiers for the purpose of classifying normal and coronary artery disease (CAD) states in previous studies [3][4][5][6].…”

“…purpose of classifying normal and coronary artery disease (CAD) states in previous studies [3][4][5][6]. Systole (heart contraction) and diastole (heart relaxation), apart from heartbeat which primarily reflects the electrical activity of the heart, are fundamental mechanical cardiac activities.…”

Time Series Classification Using Compression Distance of Recurrence Plots

“…One field of study that the use of recurrence plot is relatively common is the analysis of biological signals. Some examples are the analysis of electrocardiograms [34], electroencephalograms [9] and electromyograms [15], [30], as well detection of coronary artery disease [13].…”