Mobile Phone-Based Heart Sound Detection via Multifeatures Fusion

Heart sound, as an important physiological signal representing the state of the cardiovascular system, is crucial for the diagnosis of cardiovascular disease (CVD). Traditional heart sound detection methods rely on doctors’ experience and specialized auscultation equipment, but these methods have limitations in terms of efficiency and environmental adaptability. To address these issues, this paper proposes a mobile phone-based heart sound detection method. It uses the built-in microphone to record heart sounds, and adopts a two-stage filtering method to improve the quality of heart sounds. Next, we design a multi-feature fusion network namedMBCF-Netto extract multi-domain features, and effectively cross-domain fuse them while enhancing important channel information. We evaluate the proposed model on a self-collected smartphone heart sound dataset and two public heart sound datasets. The experimental results show that MBCF-Net achieves classification accuracies of 99.63%, 98.18%, and 99.88% on the smartphone dataset, the PhysioNet/CinC Challenge 2016 dataset and Yaseen’s five-class dataset, respectively. Compared with previous works,MBCF-Netachieves higher diagnostic accuracy, demonstrating its strong performance and adaptability across different heart sound classification scenarios.