Enhanced Heart Sound Detection via Multi-Scale Feature Extraction and Attention Mechanism Using Pitch-Shifting Data Augmentation

Abstract

Cardiovascular diseases pose a major global health threat, making early automated detection through heart sound analysis crucial for their prevention. However, existing deep learning-based heart sound detection methods have shortcomings in feature extraction, and current attention mechanisms perform inadequately in capturing key heart sound features. To address this, we first introduce a Multi-Scale Feature Extraction Network composed of Multi-Scale Inverted Residual (MIR) modules and Dynamically Gated Convolution (DGC) modules to extract heart sound features effectively. The MIR module can efficiently extract multi-scale heart sound features, and the DGC module enhances the network’s representation ability by capturing feature interrelationships and dynamically adjusting information flow. Subsequently, a Multi-Scale Attention Prediction Network is designed for heart sound feature classification, which includes a multi-scale attention (MSA) module. The MSA module effectively captures subtle pathological features of heart sound signals through multi-scale feature extraction and cross-scale feature interaction. Additionally, pitch-shifting techniques are applied in the preprocessing stage to enhance the model’s generalization ability, and multiple feature extraction techniques are used for initial feature extraction of heart sounds. Evaluated via five-fold cross-validation, the model achieved accuracies of 98.89% and 98.86% on the PhysioNet/CinC 2016 and 2022 datasets, respectively, demonstrating superior performance and strong potential for clinical application.