Detection of severe coronary artery disease based on clinical phonocardiogram and large kernel convolution interaction network

Abstract

Heart sound auscultation coupled with machine learning algorithms is a risk-free and low-cost method for coronary artery disease detection (CAD). However, current studies mainly focus on CAD screening, namely classifying CAD and non-CAD, due to limited clinical data and algorithm performance. This leaves a gap to investigate CAD severity by phonocardiogram (PCG). To solve the issue, we first establish a clinical PCG dataset for CAD patients. The dataset includes 150 subjects with 80 severe CAD and 70 non-severe CAD patients. Then, we propose the large kernel convolution interaction network (LKCIN) to detect CAD severity. It integrates automatic feature extraction and pattern classification and simplifies PCG processing steps. The developed large kernel interaction block (LKIB) has three properties: long-distance dependency, local receptive field, and channel interaction, which efficiently improves feature extraction capabilities in LKCIN. Apart from it, a separate downsampling block is proposed to alleviate feature losses during forward propagation, following the LKIBs. Experiment is performed on the clinical PCG data, and LKCIN obtains good classification performance with accuracy 85.97 %, sensitivity 85.64 %, and specificity 86.26 %. Our study breaks conventional CAD screening, and provides a reliable option for CAD severity detection in clinical practice.