Robust heart sound segmentation based on spectral change detection and genetic algorithms

Abstract

Listening to cardiac sounds can quickly provide information about the functioning of the heart. The heart sound signal, also known as the phonocardiogram (PCG), plays an essential role in automatic auscultation. Segmentation of the PCG signal into its fundamental parts can significantly facilitate any further analysis. In this work, we propose a new method that segments the PCG into fundamental heart sounds and silences. This method can be divided into two stages: Detection and Selection. In the first stage, a function whose maxima indicate the presence of sound events is generated based on the calculation of the spectral flux, a measure of how quickly the spectrum of the PCG signal is changing with respect to time. In the second stage, the position of the beginning and termination of the fundamental heart sounds is detected by analyzing and selectively choosing the time positions of the maxima in the detection function. This selection is solved as an optimization problem through the estimation of an ideal detection function, whose solution is found using two genetic algorithms: a simple genetic algorithm (SGA) and differential evolution (DE). The proposed method was evaluated using the PhysioNet/CinC Challenge dataset, comprising more than 3,000 PCGs. Our results exhibit a mean F1 score of 87.5% and 93.6% for the SGA and DE variants, respectively. The proposed system is robust and highly modular, which simplifies the reuse of specific parts to evaluate algorithm variants. The implementation of the proposed method is available as open-source software.