Application of Empirical Mode Decomposition–based Features for Analysis of Normal and Cad Heart Rate Signals

Sood, Surabhi; Kumar, Mohit; Pachori, Ram Bilas; Acharya, U. Rajendra

doi:10.1142/s0219519416400029

Cited by 44 publications

(9 citation statements)

References 33 publications

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“…The discrimination ability of the features is determined by computing the p-values using the Kruskal-Wallis (KW) test [52]. Recently, the KW test has been explored to test the statistical significance of the features in various biomedical signal analysis applications [53][54][55]. The p-values are found significantly low (p < 0.0001) for all the features (SEnt computed from 25 subband signals), which indicate good discrimination ability of all the computed features.…”

Section: Resultsmentioning

confidence: 99%

Automated Diagnosis of Myocardial Infarction ECG Signals Using Sample Entropy in Flexible Analytic Wavelet Transform Framework

Kumar

Pachori

Acharya

2017

Entropy

Self Cite

122

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Myocardial infarction (MI) is a silent condition that irreversibly damages the heart muscles. It expands rapidly and, if not treated timely, continues to damage the heart muscles. An electrocardiogram (ECG) is generally used by the clinicians to diagnose the MI patients. Manual identification of the changes introduced by MI is a time-consuming and tedious task, and there is also a possibility of misinterpretation of the changes in the ECG. Therefore, a method for automatic diagnosis of MI using ECG beat with flexible analytic wavelet transform (FAWT) method is proposed in this work. First, the segmentation of ECG signals into beats is performed. Then, FAWT is applied to each ECG beat, which decomposes them into subband signals. Sample entropy (SEnt) is computed from these subband signals and fed to the random forest (RF), J48 decision tree, back propagation neural network (BPNN), and least-squares support vector machine (LS-SVM) classifiers to choose the highest performing one. We have achieved highest classification accuracy of 99.31% using LS-SVM classifier. We have also incorporated Wilcoxon and Bhattacharya ranking methods and observed no improvement in the performance. The proposed automated method can be installed in the intensive care units (ICUs) of hospitals to aid the clinicians in confirming their diagnosis.

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Section: Resultsmentioning

confidence: 99%

Automated Diagnosis of Myocardial Infarction ECG Signals Using Sample Entropy in Flexible Analytic Wavelet Transform Framework

Kumar

Pachori

Acharya

2017

Entropy

Self Cite

122

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“…HRV signals of normal and CAD subjects are studied for different sample lengths [13]. Five features namely, Amplitude Modulation (AM) bandwidth, Frequency Modulation (FM) bandwidth, Second-Order Difference Plot (SODP), Analytic Signal Representation (ASR) area and mean frequency of Fourier-Bessel Expansion (FBE) are extracted from intrinsic mode functions.…”

Section: Related Workmentioning

confidence: 99%

An Automated Detection of CAD Using the Method of Signal Decomposition and Non Linear Entropy Using Heart Signals

Chintalapati¹,

S.V²

2019

IJIGSP

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The Coronary Artery Disease (CAD) which is one among the major class of cardiovascular diseases is emerging as an epidemic in the society and has proven to be the leading cause for more number of deaths when compared to the other cardiovascular diseases. It is emerging as one of the threats to the economy. It has become very important to detect CAD in its early stage which can help society in a broader way by saving a significant number of lives. The proposed method is a novel efficient automated approach which is capable of detecting CAD among the large group of patients using Electrocardiogram (ECG) signal. The system design provides a complete model of pre-processing of ECG, finding the heart rate which is further decomposed up to 4 level sub-bands using analytic transformation based signal decomposition method. The signal decomposition method is used to analyze the low frequency components of the signal and to deal with non stationary nature of heart signals. Two Non-linear entropy estimators as K-Nearest Neighbor (K-NN) and Correlation entropy are applied to decomposed sub-bands obtained after applying Analytic wavelet transformation based flexible decomposition technique to extract non-linear dynamics. The clinical significant features from the large data set can be selected by employing wilcoxon ranking method which assigns ranks on the applied signal. Further, an entropy-based classification approach and a suitable classifier namely Linear support vector machine (L-SVM) is used to classify among CAD and normal class. The algorithm is simulated in MATLAB and it is found that the results matched closely with the available data. This computer-assisted automated system which characterizes the heart signal can serve as an aid for the cardiologists in their daily screening of a large number of patients and can be used in primary health care centers which help the physicians in the early detection of a CAD.

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“…Heart rate variability (HRV) signals are extracted from electrocardiogram (ECG) [1], which is a noninvasive marker for monitoring an individual's health. e time interval between two consecutive R-peaks in an ECG is called an RR interval or interbeat interval.…”

Section: Introductionmentioning

confidence: 99%

“…Interbeat intervals cannot be easily analyzed using visual detection, which may lead toward inaccurate classification of normal and diseased subjects. In this regard, various techniques [1] have been developed for automated detection and prediction of normal and abnormal HRV signals, including discrete wavelet transform (DWT) and empirical mode decomposition (EMD). HRV signals have been used to diagnose coronary artery disease (CAD) automatically [13].…”

Section: Introductionmentioning

confidence: 99%

Detecting Congestive Heart Failure by Extracting Multimodal Features and Employing Machine Learning Techniques

Hussain

Awan

Aziz

et al. 2020

BioMed Research International

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The adaptability of heart to external and internal stimuli is reflected by the heart rate variability (HRV). Reduced HRV can be a predictor of negative cardiovascular outcomes. Based on the nonlinear, nonstationary, and highly complex dynamics of the controlling mechanism of the cardiovascular system, linear HRV measures have limited capability to accurately analyze the underlying dynamics. In this study, we propose an automated system to analyze HRV signals by extracting multimodal features to capture temporal, spectral, and complex dynamics. Robust machine learning techniques, such as support vector machine (SVM) with its kernel (linear, Gaussian, radial base function, and polynomial), decision tree (DT), k-nearest neighbor (KNN), and ensemble classifiers, were employed to evaluate the detection performance. Performance was evaluated in terms of specificity, sensitivity, positive predictive value (PPV), negative predictive value (NPV), and area under the receiver operating characteristic curve (AUC). The highest performance was obtained using SVM linear kernel (TA = 93.1%, AUC = 0.97, 95% CI [lower bound = 0.04, upper bound = 0.89]), followed by ensemble subspace discriminant (TA = 91.4%, AUC = 0.96, 95% CI [lower bound 0.07, upper bound = 0.81]) and SVM medium Gaussian kernel (TA = 90.5%, AUC = 0.95, 95% CI [lower bound = 0.07, upper bound = 0.86]). The results reveal that the proposed approach can provide an effective and computationally efficient tool for automatic detection of congestive heart failure patients.

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Application of Empirical Mode Decomposition–based Features for Analysis of Normal and Cad Heart Rate Signals

Cited by 44 publications

References 33 publications

Automated Diagnosis of Myocardial Infarction ECG Signals Using Sample Entropy in Flexible Analytic Wavelet Transform Framework

Automated Diagnosis of Myocardial Infarction ECG Signals Using Sample Entropy in Flexible Analytic Wavelet Transform Framework

An Automated Detection of CAD Using the Method of Signal Decomposition and Non Linear Entropy Using Heart Signals

Detecting Congestive Heart Failure by Extracting Multimodal Features and Employing Machine Learning Techniques

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