TY - JOUR
T1 - BBANet
T2 - Bilateral biological auditory-inspired neural network for heart sound classification
AU - Tan, Yang
AU - Zhang, Haojie
AU - Xu, Jingwen
AU - Wu, Hanhan
AU - Qian, Kun
AU - Hu, Bin
AU - Yamamoto, Yoshiharu
AU - Schuller, Björn W.
N1 - Publisher Copyright:
Copyright © 2025. Published by Elsevier Ltd.
PY - 2026/1/15
Y1 - 2026/1/15
N2 - 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 .
AB - 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 .
KW - Auditory pathway
KW - Biological auditory-inspired
KW - Cochlea-like module
KW - Frequency-channel-spatial attention
KW - Heart sound classification
UR - https://www.scopus.com/pages/publications/105022238500
U2 - 10.1016/j.engappai.2025.113165
DO - 10.1016/j.engappai.2025.113165
M3 - Article
AN - SCOPUS:105022238500
SN - 0952-1976
VL - 164
JO - Engineering Applications of Artificial Intelligence
JF - Engineering Applications of Artificial Intelligence
M1 - 113165
ER -