Publication Type:

Journal Article

Source:

IEEE Sensors Journal, Institute of Electrical and Electronics Engineers Inc., Volume 17, Number 12, p.3861-3872 (2017)

URL:

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85021744815&doi=10.1109%2fJSEN.2017.2694970&partnerID=40&md5=0ab6c2bd3c584f1d4ba2828f2ce8c67f

Keywords:

Biomedical signal processing, Blood vessels, Boundary determination, Cardiology, Classification accuracy, Classification methods, Classification of heart sounds, Database systems, Electronic stethoscope, extraction, Heart, heart murmur, Heart sounds, Parameter extraction, Phonocardiograms, Phonocardiography, Signal detection, Signal processing, Wavelet decomposition, Wavelet transforms

Abstract:

Accurate measurement of heart sound and murmur parameters is of great importance in the automated analysis of phonocardiogram (PCG) signals. In this paper, we propose a novel unified PCG signal delineation and murmur classification method without the use of reference signal for automatic detection and classification of heart sounds and murmurs. The major components of the proposed method are the empirical wavelet transform-based PCG signal decomposition for discriminating heart sounds from heart murmurs and suppressing background noises, the Shannon entropy envelope extraction, the instantaneous phase-based boundary determination, heart sound and murmur parameter extraction, the systole/diastole discrimination and the decision rules-based murmur classification. The accuracy and robustness of the proposed method is evaluated using a wide variety of normal and abnormal PCG signals taken from the standard PCG databases, including PASCAL heart sounds challenge database, PhysioNet/CinC challenge heart sound database, and real-time PCG signals. Evaluation results show that the proposed method achieves an average sensitivity (Se) of 94.38%, positive predictivity (Pp) of 97.25%, and overall accuracy (OA) of 91.92% for heart sound segmentation and Se of 97.58%, Pp of 96.46%, and OA of 94.21% in detecting the presence of heart murmurs for SNR of 10 dB. The method yields an average classification accuracy of 95.5% for the PCG signals with SNR of 20 dB. Results show that the proposed method outperforms other existing heart sound segmentation and murmur classification methods. © 2017 IEEE.

Notes:

cited By 0

Cite this Research Publication

N. V. Varghees and Dr. K. I. Ramachandran, “Effective heart sound segmentation and murmur classification using empirical wavelet transform and instantaneous phase for electronic stethoscope”, IEEE Sensors Journal, vol. 17, pp. 3861-3872, 2017.

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