Application of empirical mode decomposition for analysis of normal and diabetic RR-interval signals

Pachori, Ram Bilas; Avinash, Pakala; Shashank, K J; Sharma, Rajeev; Acharya, U. Rajendra

doi:10.1016/j.eswa.2015.01.051

Cited by 80 publications

(24 citation statements)

References 55 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

123

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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

123

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“…The Kruskal-Wallis statistical test [57] has been applied for determining the discrimination ability of features extracted from epileptic seizure EEG signals [58][59][60] and RR-interval (interval between adjacent QRS complexes of electrocardiogram) signals [61]. In order to examine the class discrimination ability of the features, the Kruskal-Wallis statistical test is applied on all features, and the resultant p-values are shown in Table 1.…”

Section: Resultsmentioning

confidence: 99%

An Integrated Index for the Identification of Focal Electroencephalogram Signals Using Discrete Wavelet Transform and Entropy Measures

Sharma

Pachori

Acharya

2015

Entropy

Self Cite

171

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The dynamics of brain area influenced by focal epilepsy can be studied using focal and non-focal electroencephalogram (EEG) signals. This paper presents a new method to detect focal and non-focal EEG signals based on an integrated index, termed the focal and non-focal index (FNFI), developed using discrete wavelet transform (DWT) and entropy features. The DWT decomposes the EEG signals up to six levels, and various entropy measures are computed from approximate and detail coefficients of sub-band signals. The computed entropy measures are average wavelet, permutation, fuzzy and phase entropies. The proposed FNFI developed using permutation, fuzzy and Shannon wavelet entropies is able to clearly discriminate focal and non-focal EEG signals using a single number. Furthermore, these entropy measures are ranked using different techniques, namely the Bhattacharyya space algorithm, Student's t-test, the Wilcoxon test, the receiver operating characteristic (ROC) and entropy. These ranked features are fed to various classifiers, namely k-nearest neighbour (KNN), probabilistic neural network (PNN), fuzzy classifier and least squares support vector machine (LS-SVM), for automated classification of focal and non-focal EEG signals using the minimum number of features. The identification of the focal EEG signals can be helpful to locate the epileptogenic focus.Entropy 2015, 17 5219

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“…However, Acharya et al [5] reported 90% of accuracy, 92.5% of sensitivity and 88.7% of specificity with AdaBoost classifier coupled with four nonlinear features. Pachori et al [42] (In press), proposed a new nonlinear method based on Empirical Mode Decomposition (EMD) to discriminate between normal and diabetic RR-interval signals. In their proposed method, EMD decomposes the RR-interval signal into IMFs from which five features (Fourier-Bessel series expansion, amplitude modulation bandwidth, frequency modulation bandwidths, analytic signal representation and second order difference Plot) are extracted.…”

Section: Introductionmentioning

confidence: 99%

Computer-aided diagnosis of diabetic subjects by heart rate variability signals using discrete wavelet transform method

Acharya

Vidya

Ghista³

et al. 2015

Knowledge-Based Systems

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Application of empirical mode decomposition for analysis of normal and diabetic RR-interval signals

Cited by 80 publications

References 55 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 Integrated Index for the Identification of Focal Electroencephalogram Signals Using Discrete Wavelet Transform and Entropy Measures

Computer-aided diagnosis of diabetic subjects by heart rate variability signals using discrete wavelet transform method

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