Classification of heart sound based on s-transform and neural network
Cardiovascular diseases (CVDs) are currently the leading cause of deaths worldwide. The traditional auscultation is cost-effective and time-saving for the public to diagnose CVDs early. While many approaches for analysis of the heart sound (HS) signal from auscultation have been utilized successfully, few studies are focused on acoustic perspective to interpret the HS signal. This paper creatively proposes a multidimensional feature extraction technique based on timbre model to interpret HS, which stems from clinical diagnostic basis and medical knowledge. The extracted features have three dimensions, including spectral centroid (SC), log attack time (LT) and temporal centroid (TC). The simulation experiments indicate that the proposed method is promising in HS feature extraction and the later CVD diagnosis.