A Segmentation-Free Model for Heart Sound Feature Extraction

Abstract

Currently, the fatality of cardiovascular diseases (CVDs) represents one of the global primary healthcare challenges and necessitates broader population checking for earlier intervention. The traditional auscultation is cost-effective and time-saving for broader population to diagnose CVDs early. While many approaches in analyzing heart sound (HS) signal from auscultation have been utilized successfully, few studies are focused on acoustic perspective to interpret the HS signal. This paper proposes a segmentation-free model that can interpret HS effectively, which aligns engineering with clinical diagnostic basis and medical knowledge much more. The presented model stems from timbre analysis model but is adapted for HS signal. The relevant theoretical analysis and simulation experiments indicate that the proposed method has good performance in HS analysis.