Respiratory Sound Classification: From Fluid-Solid Coupling Analysis to Feature-Band Attention

Abstract

Based on respiratory sound production mechanisms, we study the relationship between airflow characteristics in the bronchi and sound pressure spectrum curves to implement an end-to-end respiratory sound classification system with a feature-band attention module. First, we analyse fluid-solid coupling simulations of the bronchi and execute acoustic simulations to obtain the spectrum curves of the bronchi at the sound pressure level. Then, based on the spectrum characteristics of the bronchi, we propose an attention strategy to refine the acoustic features with adaptive weights. In addition, we introduce a feature-band attention module to ResNet-based networks with a squeeze-and-excitation block. Finally, we perform experiments on the ICBHI public database to classify respiratory sounds into one of four classes: normal, wheezes, crackles, and both (wheezes and crackles). The results show that our proposed system exhibits superior performance compared with the baseline system. This type of feature learning strategy is useful for exploring the distinct characteristics of different types of respiratory sounds.

Keywords

• Fluid-solid coupling
• attention learning
• end-to-end system
• respiratory sound classification
• squeeze-and-excitation