Publication Type:

Conference Paper


2016 IEEE Students' Technology Symposium, TechSym 2016, Institute of Electrical and Electronics Engineers Inc., p.305-310 (2017)





Biometric authentication, Cardiology, Diagnostic systems, Envelope extraction, Heart, Heart sound analysis, Heart sound signal, Identification accuracy, Network function virtualization, Phonocardiography, Photo-plethysmogram, Signal processing, Stationary wavelet transforms, Wavelet decomposition, Wavelet transforms


<p>Phonocardiogram (PCG) segmentation is the crucial first step in automated heart sound analysis and diagnostic systems. Recently, the cardiac signals (including, electrocardiogram, phonocardiogram and photoplethysmogram) are simultaneously recorded for most cardiac signal processing applications such as cardiovascular diagnostic system, biometric authentication, and emotion/stress recognition. In this paper, we present an effective two-channel heart sound segmentation framework using PCG and pulse signals. The proposed framework comprises the steps of: heart sound signal decomposition using stationary wavelet transform, Shannon entropy envelope extraction, heart sound endpoint determination, systolic peak detection, and heart sound discrimination. The proposed framework is tested and validated using the simultaneously recorded heart sound and pulse signals. Performance evaluation results demonstrate that the proposed heart sound endpoint and systolic peak detection methods can achieves an average Se of 98.98%, +P of 96.80% and Se of 99.57%, +P of 99.37%, respectively. The proposed framework achieves an identification accuracy of 100% in distinguishing the first heart sound (S1) and second heart sound (S2) under clean and noisy signal conditions. © 2016 IEEE.</p>


cited By 0; Conference of 4th IEEE Students' Technology Symposium, TechSym 2016 ; Conference Date: 30 September 2016 Through 2 October 2016; Conference Code:126773

Cite this Research Publication

V. N. Varghees and Dr. K. I. Ramachandran, “Two-channel heart sound segmentation framework using phonocardiogram and pulsatile signals”, in 2016 IEEE Students' Technology Symposium, TechSym 2016, 2017, pp. 305-310.