Classification of Respiratory Conditions using Auscultation Sound

Quan T. Do; Kirill Lipatov; Hsin Yi Wang; Brian W. Pickering; Vitaly Herasevich

doi:10.1109/EMBC46164.2021.9630294

Classification of Respiratory Conditions using Auscultation Sound

Quan T. Do, Kirill Lipatov, Hsin Yi Wang, Brian W. Pickering, Vitaly Herasevich

Anesthesiology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

Management of respiratory conditions relies on timely diagnosis and institution of appropriate management. Computerized analysis and classification of breath sounds has a potential to enhance reliability and accuracy of diagnostic modality while making it suitable for remote monitoring, personalized uses, and self-management uses. In this paper, we describe and compare sound recognition models aimed at automatic diagnostic differentiation of healthy persons vs patients with COPD vs patients with pneumonia using deep learning approaches such as Multi-layer Perceptron Classifier (MLPClassifier) and Convolutional Neural Networks (CNN).Clinical Relevance-Healthcare providers and researchers interested in the field of medical sound analysis, specifically automatic detection/classification of auscultation sound and early diagnosis of respiratory conditions may benefit from this paper.

Original language	English (US)
Title of host publication	43rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2021
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1942-1945
Number of pages	4
ISBN (Electronic)	9781728111797
DOIs	https://doi.org/10.1109/EMBC46164.2021.9630294
State	Published - 2021
Event	43rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2021 - Virtual, Online, Mexico Duration: Nov 1 2021 → Nov 5 2021

Publication series

Name	Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
ISSN (Print)	1557-170X

Conference

Conference	43rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2021
Country/Territory	Mexico
City	Virtual, Online
Period	11/1/21 → 11/5/21

ASJC Scopus subject areas

Signal Processing
Biomedical Engineering
Computer Vision and Pattern Recognition
Health Informatics

Access to Document

10.1109/EMBC46164.2021.9630294

Cite this

Do, Q. T., Lipatov, K., Wang, H. Y., Pickering, B. W., & Herasevich, V. (2021). Classification of Respiratory Conditions using Auscultation Sound. In 43rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2021 (pp. 1942-1945). (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/EMBC46164.2021.9630294

Classification of Respiratory Conditions using Auscultation Sound. / Do, Quan T.; Lipatov, Kirill; Wang, Hsin Yi et al.
43rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2021. Institute of Electrical and Electronics Engineers Inc., 2021. p. 1942-1945 (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Do, QT, Lipatov, K, Wang, HY, Pickering, BW & Herasevich, V 2021, Classification of Respiratory Conditions using Auscultation Sound. in 43rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2021. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, Institute of Electrical and Electronics Engineers Inc., pp. 1942-1945, 43rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2021, Virtual, Online, Mexico, 11/1/21. https://doi.org/10.1109/EMBC46164.2021.9630294

Do QT, Lipatov K, Wang HY, Pickering BW , Herasevich V. Classification of Respiratory Conditions using Auscultation Sound. In 43rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2021. Institute of Electrical and Electronics Engineers Inc. 2021. p. 1942-1945. (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS). doi: 10.1109/EMBC46164.2021.9630294

Do, Quan T. ; Lipatov, Kirill ; Wang, Hsin Yi et al. / Classification of Respiratory Conditions using Auscultation Sound. 43rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2021. Institute of Electrical and Electronics Engineers Inc., 2021. pp. 1942-1945 (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS).

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abstract = "Management of respiratory conditions relies on timely diagnosis and institution of appropriate management. Computerized analysis and classification of breath sounds has a potential to enhance reliability and accuracy of diagnostic modality while making it suitable for remote monitoring, personalized uses, and self-management uses. In this paper, we describe and compare sound recognition models aimed at automatic diagnostic differentiation of healthy persons vs patients with COPD vs patients with pneumonia using deep learning approaches such as Multi-layer Perceptron Classifier (MLPClassifier) and Convolutional Neural Networks (CNN).Clinical Relevance-Healthcare providers and researchers interested in the field of medical sound analysis, specifically automatic detection/classification of auscultation sound and early diagnosis of respiratory conditions may benefit from this paper.",

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AB - Management of respiratory conditions relies on timely diagnosis and institution of appropriate management. Computerized analysis and classification of breath sounds has a potential to enhance reliability and accuracy of diagnostic modality while making it suitable for remote monitoring, personalized uses, and self-management uses. In this paper, we describe and compare sound recognition models aimed at automatic diagnostic differentiation of healthy persons vs patients with COPD vs patients with pneumonia using deep learning approaches such as Multi-layer Perceptron Classifier (MLPClassifier) and Convolutional Neural Networks (CNN).Clinical Relevance-Healthcare providers and researchers interested in the field of medical sound analysis, specifically automatic detection/classification of auscultation sound and early diagnosis of respiratory conditions may benefit from this paper.

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Classification of Respiratory Conditions using Auscultation Sound

Abstract

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