Detection of Valvular Heart Diseases From PCG Signals Using Machine and Deep Learning Models: A Review

Artificial intelligence (AI) predictions are widely used to address challenges in the heart health sector, such as providing clinical decision support. Early detection of valvular heart disease (VHD) is effective in reducing critical cardiac problems and sudden death. This review proposes investigating methods for automatically diagnosing heart disease from phonocardiogram (PCG) signals using various advanced Machine Learning (ML) and Deep Learning (DL) models. This study also aimed to provide an overview of ongoing research on PCG signal processing and to pinpoint areas that warrant additional investigation. Several Scopus-indexed research forums, such as IEEE, Science Direct, Frontiers, MDPI, and Computing in Cardiology, as well as databases such as the Michigan Heart Sound Library (MHSL), Github, and Physio-Net on the classification of AI-related PCG signals, were considered to construct this review with 199 relevant research articles covering the period from 2016 to 2024. The early diagnosis and prediction of heart valve disease are the domains in which machine learning and deep learning models were most commonly used. The performance of earlier detection has increased significantly according to advanced techniques of PCG signal classification. A limited number of studies have compared and analyzed categorization measures such as F score, sensitivity, accuracy, precision, and specificity. However, a mean increase in the predicted accuracy was observed, depending on the various advanced techniques and classifiers used.