Lightweight network coupled with heart sounds to evaluate exercise-induced cardiac fatigue

Abstract

Exercise-induced cardiac fatigue (EICF), namely a transient decline in ventricular systolic/diastolic function, usually happens in arduous endurance athletes, which increases the incidence of heart diseases. This paper proposes a computer-aided heart sound analysis method to evaluate EICF based on myocardial systolic/diastolic force, a crucial indicator for EICF. First, resting and post-exercise heart sounds were collected as dataset, and a lightweight channel grouping attention network (CGAN) was proposed to recognize abnormal heart sounds. Then, according to the data characteristic, an asymmetric depthwise separable convolution (ADSC) block was developed to extract features. The ADSC block designed based on channel grouping which can effectively reduce parameters. Finally, a channel attention module was developed to focus on kernel data features, and improved classification accuracy with negligible parameters. The CGAN achieved superior classification performance with fewer parameters, and the accuracy, precision, and recall were 96.91%, 97.01%, and 96.44% respectively. Experimental results show that our CGAN-aided heart sound analysis provides an accurate, objective, and affordable alternative to the evaluation of EICF.