Automated Heart Sound Activity Detection From PCG Signal Using Time–Frequency-Domain Deep Neural Network

Abstract

The phonocardiogram (PCG) signal deciphers the mechanical activity of the heart, and it consists of the fundamental heart sounds (S1 and S2), murmurs, and other associated sounds (S3 and S4). Detection of fundamental heart sound activity (FHSA) is vital for the automated analysis of PCG signals to diagnose various heart valve diseases. This paper proposes a time-frequency domain (TFD) deep neural network (DNN) approach for automated FHSA detection using PCG signals. The modified Gaussian window-based Stockwell Transform (MGWST) is used to obtain the time-frequency representation (TFR) of PCG signals. The Shannon-Teager-Kaiser Energy (STKE), smoothing, and thresholding techniques are then employed to evaluate the segmented heart sound components. The TFD Shannon entropy (TFDSE) features are computed from the segmented heart sound components of the PCG signal. The deep neural network (DNN) developed based on the stacked autoencoders (SAEs) is used for automated identification of FHSA components. The performance of the proposed approach is evaluated using two publicly available standard databases (Database 1: Michigan heart sound and murmur database and Database 2: PhysioNet Computing in Cardiology Challenge 2016). The results demonstrate that the proposed approach has achieved the accuracy, sensitivity, specificity, and precision values of 99.55 %, 99.93 %, 99.26 %, 99.02 % for Database 1, and 95.43 %, 97.92 %, 98.32 %, 97.60 % for Database 2, respectively. It is shown that, the proposed FHSA detection approach has obtained better accuracy than existing methods.