Automatic Classification of Respiratory Sound Considering Hierarchical Structure

Manami Tabata; Huimin Lu; Tohru Kamiya; Shingo Mabu; Shoji Kido

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자료유형: 학술대회자료

저자정보: Manami Tabata (Kyushu Institute of Technology) Huimin Lu (Kyushu Institute of Technology) Tohru Kamiya (Kyushu Institute of Technology) Shingo Mabu (Yamaguchi University) Shoji Kido (Osaka University)

저널정보: 제어로봇시스템학회 제어로봇시스템학회 국제학술대회 논문집 ICCAS 2022

발행연도: 2022.11

수록면: 537 - 541 (5page)

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Respiratory diseases are one of the leading causes of death worldwide. Approximately 8 million people die annually from respiratory diseases. Diagnosis is made primarily by auscultation using a stethoscope. The lack of quantitative criteria makes diagnosis difficult in the field where physicians are in short supply. To solve this problem, a computer aided diagnosis (CAD) system that quantitatively analyzes and classifies respiratory sounds and outputs them as a "second opinion" is needed. In this paper, HPSS (Harmonious / Percussive Sound Separation) is used to separate abnormal respiratory sound features. Images are generated from the spectral envelopes obtained by linear prediction coefficients (LPC) for each of the three types of respiratory sound data before separation. The CNN (convolutional neural networks) framework based on hierarchical structure of the correct labels is introduced. The proposed method was applied to the dataset used in the International Conference on Biomedical and Health Informatics (ICBHI) 2017 Challenge. As a result, we obtained a sensitivity of 63.5%, specificity of 85.1%, average score of 74.3%, harmonic score of 72.7%, area under the curve of 87.8%, and false negative rate of 24.5%, respectively.

#Respiratory Sounds #Branch Convolutional Neural Network #Computer Aided Diagnosis #Linear Predictive Coefficient #Harmonious / Percussive Sound Separation

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