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RISS 인기검색어
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- 저자 : 정기원 (검색결과 286 건)
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Feature Extraction and Deep Learning Model for Respiratory Sound Analysis
정기원,황하은,김성범 한국품질경영학회 2021 한국품질경영학회 학술대회 Vol.2021 No.-
In the medical field, doctors diagnose respiratory disease by auscultating the patient’s respiratory sound. It means that the subjective judgment of the doctor is diagnosed with respiratory diseases rather than the quantitative assessment method. However, the subjective diagnosis method relies on the experience of the doctor and can lead t misdiagnosed results. To address these issues, it is important to derive quantitative indicators based on the analysis of respiratory sound data ad utilize it as an objective aid for the diagnosis of the doctor. In this study, we propose using a Hierarchical Attention Network (HAN) model for respiratory sound analysis. This method can reflect hierarchical patterns of respiratory sounds that consist of time and frequency domain and allows doctors to interpret the important feature of respiratory sounds. In addition, we propose a feature extraction method that applies several features of respiratory sound as stacked channels. We conducted experiments on real-world respiratory sound data to demonstrate the effectiveness and applicability of our method. The experimental results showed that the proposed method outperformed the existing methods for respiratory sound analysis. We believe that the proposed method can contribute to diagnosis in the medical field and various industries where the interpretation of sound data is important.
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