MCAF-Net: A non-invasive early screening method for coronary artery disease based on a multi-scale cross-modal model

¹ State key Laboratory of Extreme Environment Optoelectronic Dynamic Testing Technology and Instrument, North University of China, Taiyuan, 030051, China
² Cardiovascular Department, the Second Hospital of Shanxi Medical University, Shanxi Medical University, Taiyuan, 030001, China

^* Corresponding author: This email address is being protected from spambots. You need JavaScript enabled to view it.

Abstract

Coronary artery disease (CAD) is one of the leading causes of death worldwide. Achieving early, non-invasive, and highly accurate detection of CAD is crucial for reducing its mortality rate. This study utilizes a high-sensitivity MEMS-based PCG-ECG Synchronous Auscultation System to construct a high-fidelity clinical dataset of synchronous PCG-ECG data for CAD. Leveraging deep learning technology, we developed a Multi-scale Cross-modal Attention Fusion Network (MCAF-Net). This network extracts multi-resolution features from synchronous PCG-ECG spectrograms using an improved residual network and performs feature interaction and fusion through Mutual Cross Attention (MCA), achieving high-precision detection of CAD (96.29% on the clinical dataset, 97.69% on the public dataset).