Coronary Artery Disease Classification Algorithms and System Integration Based on Synchronized PCG and ECG Signals.

Coronary artery disease (CAD) has become a major threat to human health. Under the menace of high morbidity and mortality rates, the adoption of precise, non-invasive, and low-cost detection methods for the early screening and prevention of CAD appears particularly crucial. This paper, based on a high-sensitivity MEMS-based PCG-ECG Synchronous Auscultation System, constructed a clinical synchronous PCG-ECG dataset. Furthermore, a Swin-TFAM mid-fusion multimodal CAD classification model was developed, achieving high-precision classification of CAD. Swin-TFAM was ultimately integrated into the MEMS-based PCG-ECG Synchronous Auscultation System, providing a novel solution for high-sensitivity, non-invasive detection and intelligent diagnosis of CAD.