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Entropy 2017, 19(3), 92; doi:10.3390/e19030092

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

1
Discipline of Electrical Engineering, Indian Institute of Technology Indore, Indore 453552, India
2
Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore 599489, Singapore
3
Department of Biomedical Engineering, School of Science and Technology, SIM University, Singapore 599491, Singapore
4
Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur 50603, Malaysia
*
Author to whom correspondence should be addressed.
Academic Editor: Herbert Jelinek
Received: 25 January 2017 / Revised: 15 February 2017 / Accepted: 16 February 2017 / Published: 27 February 2017
(This article belongs to the Special Issue Entropy and Cardiac Physics II)
View Full-Text   |   Download PDF [428 KB, uploaded 28 February 2017]   |  

Abstract

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. View Full-Text
Keywords: CHF; HRV; FAWT; accumulated entropy; classifier CHF; HRV; FAWT; accumulated entropy; classifier
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Kumar, M.; Pachori, R.B.; Acharya, U.R. Use of Accumulated Entropies for Automated Detection of Congestive Heart Failure in Flexible Analytic Wavelet Transform Framework Based on Short-Term HRV Signals. Entropy 2017, 19, 92.

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