Review of Phonocardiogram Signal Analysis: Insights from the PhysioNet/CinC Challenge 2016 Database

Abstract

The phonocardiogram (PCG) is a crucial tool for the early detection, continuous monitoring, accurate diagnosis, and efficient management of cardiovascular diseases. It has the potential to revolutionize cardiovascular care and improve patient outcomes. The PhysioNet/CinC Challenge 2016 database, a large and influential resource, encourages contributions to accurate heart sound state classification (normal versus abnormal), achieving promising benchmark performance (accuracy: 99.80%; sensitivity: 99.70%; specificity: 99.10%; and score: 99.40%). This study reviews recent advances in analytical techniques applied to this database, and 104 publications on PCG signal analysis are retrieved. These techniques encompass heart sound preprocessing, signal segmentation, feature extraction, and heart sound state classification. Specifically, this study summarizes methods such as signal filtering and denoising; heart sound segmentation using hidden Markov models and machine learning; feature extraction in the time, frequency, and time-frequency domains; and state-of-the-art heart sound state recognition techniques. Additionally, it discusses electrocardiogram (ECG) feature extraction and joint PCG and ECG heart sound state recognition. Despite significant technical progress, challenges remain in large-scale high-quality data collection, model interpretability, and generalizability. Future directions include multi-modal signal fusion, standardization and validation, automated interpretation for decision support, real-time monitoring, and longitudinal data analysis. Continued exploration and innovation in heart sound signal analysis are essential for advancing cardiac care, improving patient outcomes, and enhancing user trust and acceptance.