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

• Published in: IEEE transactions on instrumentation and measurement Vol. 71; pp. 1 - 10
• Main Authors: Ghosh, Samit Kumar, Ponnalagu, R. N., Tripathy, Rajesh Kumar, Panda, Ganapati, Pachori, Ram Bilas
• Format: Journal Article
• Language: English
• Published: New York IEEE 2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Accuracy; Acoustics; Artificial neural networks; Automation; Cardiology; deep neural network (DNN); Entropy (Information theory); Frequency domain analysis; fundamental heart sound~(FHS) recognition; Heart; Heart valves; modified Stockwell transform (ST); Neural networks; phonocardiogram (PCG); Phonocardiography; Recording; Smoothing methods; Sound; stacked autoencoder (SAE); Time-frequency analysis
• ISSN: 0018-9456; 1557-9662
• DOI: 10.1109/TIM.2022.3192257

The phonocardiogram (PCG) signal deciphers the mechanical activity of the heart, and it consists of the fundamental heart sounds (FHSs) (S1 and S2), murmurs, and other associated sounds (S3 and S4). Detection of FHS activity (FHSA) is vital for the automated analysis of PCG signals to diagnose various heart valve diseases. This article 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 DNN developed based on the stacked autoencoders (SAEs) is used for the 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%, and 99.02% for Database 1 and 95.43%, 97.92%, 98.32%, and 97.60% for Database 2, respectively. It is shown that the proposed FHSA detection approach has obtained better accuracy than existing methods.