Heart Sound Segmentation Using Deep Learning Techniques
This addresses heart disease diagnosis through enhanced auscultation, but it is incremental as it builds on prior segmentation techniques.
The paper tackled heart sound segmentation and classification into S1 and S2 sounds using a novel approach, achieving superior performance on the PASCAL dataset compared to existing methods.
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.