이상 호흡음 분류를 위한 계층적 어텐션 네트워크 모델

Abstract

Recently the diagnosis of respiratory diseases based on respiratory-sound data has drawn much attention. However, no quantitative assessment method exists to detect abnormal respiratory sounds when making a diagnosis. Although existing studies have attempted to support doctors by providing them with machine learning results, there were limitations on explaining causal symptoms and establishing quantitative assessment. Therefore, a reliable method that can diagnose and explain the causal symptoms based on respiratory sound data is required. In this study, we propose using a hierarchical attention network for detecting abnormal respiratory sounds. The hierarchical attention network reflects hierarchical patterns of respiratory sounds and allows us to interpret the important feature of respiratory sounds. The experimental results showed that hierarchical attention network performed better than other existing methods in terms of sensitivity (ability of correctly detecting abnormal respiratory sounds) and explainability. We believe that the framework presented in this study can not only serve as a “second opinion” that can help doctors diagnose existing respiratory diseases, but also help doctors’ future research on unidentified respiratory diseases.