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.

翻译：心脏病仍然是全球范围内导致死亡的主要原因。听诊（即倾听心音的过程）可通过基于心音图（PCG）信号的计算机辅助分析得到增强。本文提出了一种新颖的心音分割与分类方法，将心音分为S1（第一心音）和S2（第二心音）。我们采用基于快速傅里叶变换（FFT）的滤波、动态规划进行事件检测，以及孪生网络进行稳健分类。与现有方法相比，我们的方法在PASCAL心音数据集上展现了更优的性能。