BBANet: Bilateral biological auditory-inspired neural network for heart sound classification

Cardiovascular Diseases (CVDs) pose a significant global health challenge. Heart sound classification using computer audition technology holds promise for rapid early screening of CVDs, saving healthcare resources and reducing detection costs. However, there is a gap in artificial intelligence models for simulating expert auscultation based on biological hearing mechanisms. In this study, we develop a multi-frequency-channel-spatial-aware bilateral biological auditory-inspired neural network (BBANet) to fully exploit and integrate inter-layer and left–right information for the short heart sound cycles classification. The bilateral network involves a cochlea-like module and a multi-frequency-channel-spatial-aware attention module in the left and right pathways, and two information fusion modules between the layers. Experimental results show that the BBANet achieves a sensitivity of 92.71%, a specificity of 97.19%, and 96.22% of accuracy and 94.95% of Unweighted Average Recall (UAR, balanced accuracy). The proposed BBANet outperforms the reimplemented state-of-the-art models and the public classical models by 7.41% and 3.84% in average UAR, respectively, which demonstrates the classification potential of models based on biological auditory principles. More importantly, BBANet has the potential to reconstruct the structure of the auditory cortex based on biological auditory transmission and processing within and between the left and right auditory pathways. Therefore, this work helps to bring computer neural networks closer to the human biological auditory system, offering the possibility of reconstructing the auditory cortex from neural networks to better simulate and understand the complex computing mechanisms of the human auditory system. Code has been made available at https://github.com/tanyang89/BBANet .