Automatic identification of abnormal lung sounds using time-frequency analysis and convolutional neural network

Name: Rattanathon Phettom

Organization : Chiang Mai University. Biomedical Engineering Institute, Chiang Mai University, and Graduated School

Name: Nipon Theera-Umpon

Organization : Chiang Mai University. Department of Electrical Engineering

Name: Sansanee Auephanwiriyakul

Organization : Chiang Mai University. Department of Electrical Engineering

keyword: Lung sound

LCSH: Neural networks (Computer science)

; Breath sound identification

; Abnormal lung sound

; Short-time Fourier transform

; Convolutional neural networks

Abstract: This research focuses on the development of a method utilizing signal processing and machine learning techniques to identify abnormal lung sounds, specifically adventitious lung sounds, for diagnosis and monitoring. The proposed algorithm combines short-time Fourier transform (STFT) with convolutional neural networks (CNN) to automatically analyze breath sounds captured by a stethoscope. By employing a band pass filter, noise is effectively r emoved, facilitating accurate identification of lung sounds. The algorithm classifies abnormal lung sounds, such as crackles and wheezes, with an impressive accuracy rate of 85.27%. This research not only enhances the efficiency of physical examinations but also enables the recording and analysis of lung sounds, thereby offering valuable insights into the progression of treatments. Furthermore, the development of this medical device has significant implications for advancing human healthcare and information retrieval in the field of respiratory medicine

King Mongkut's University of Technology North Bangkok. Central Library

Address: BANGKOK

Email: library@kmutnb.ac.th

Created: 2023

Modified: 2024-12-23

Issued: 2024-12-23

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BibliograpyCitation : In IEEE Computational Intelligence Society Thailand Chapter, King Mongkut's Institute of Technology Ladkrabang. School of Information Technology and Universitas Gadjah Mada. Department of Electrical Engineering and Information Technology. The 15th International Conference on Information Technology and Electrical Engineering (ICITEE 2023) (pp.281-286) Nonthaburi : IEEE Computational Intelligence Society Thailand Chapter, 2023

eng

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