Table of Contents
Journal of Medical Engineering
Volume 2015, Article ID 327534, 9 pages
Research Article

An Irregularity Measurement Based Cardiac Status Recognition Using Support Vector Machine

Department of Electronics and Communication Engineering, Maulana Azad National Institute of Technology, Bhopal 462003, India

Received 31 May 2015; Revised 6 September 2015; Accepted 7 October 2015

Academic Editor: Yuemin Zhu

Copyright © 2015 Poulami Banerjee and Ashok Mondal. 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.


An automated robust feature extraction technique is proposed in this paper based on inherent structural distribution of heart sound to analyze the phonocardiogram signal in presence of environmental noise and interference of lung sound signal. The structural complexity of the heart sound signal is estimated in terms of sample entropy using a nonlinear signal processing framework. The effectiveness of the feature is evaluated using a support vector machine under two different circumstances which include Gaussian noise and pulmonary perturbation. The analysis framework has been executed on a composite data set of 60 healthy and 60 pathological individuals for different SNR levels (−5 to 10 dB) and the performance accuracy is close to that of the clean signal. In addition, a comparative study has been done with conventional approaches which includes waveform analysis, spectral domain inspection, and spectrogram evaluation. The experimental results show that sample entropy based classification method gives an accuracy of 96.67% for clean data and 91.66% for noisy data of SNR 10 dB. The result suggests that the proposed method performs significantly well over the visual and audio test.