Heart Sound Segmentation Using Deep Learning Techniques

TL;DR

This paper presents a novel approach for heart sound segmentation and classification into S1 (LUB) and S2 (DUB) sounds that employs FFT-based filtering, dynamic programming for event detection, and a Siamese network for robust classification.

Abstract

Heart disease remains a leading cause of mortality worldwide. Auscultation, the process of listening to heart sounds, can be enhanced through computer-aided analysis using Phonocardiogram (PCG) signals. This paper presents a novel approach for heart sound segmentation and classification into S1 (LUB) and S2 (DUB) sounds. We employ FFT-based filtering, dynamic programming for event detection, and a Siamese network for robust classification. Our method demonstrates superior performance on the PASCAL heart sound dataset compared to existing approaches.

Figures (6)

• Figure 1: FFT of PCG
• Figure 2: FFT of PCG
• Figure 3: Butter-worth filtered PCG
• Figure 4: FFT filtered PCG
• Figure 5: $\lvert F(\Delta t, \tau) \rvert$