TY - GEN
T1 - Classification of Respiratory Conditions using Auscultation Sound
AU - Do, Quan T.
AU - Lipatov, Kirill
AU - Wang, Hsin Yi
AU - Pickering, Brian W
AU - Herasevich, Vitaly
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021
Y1 - 2021
N2 - 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.
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.
UR - http://www.scopus.com/inward/record.url?scp=85122520789&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85122520789&partnerID=8YFLogxK
U2 - 10.1109/EMBC46164.2021.9630294
DO - 10.1109/EMBC46164.2021.9630294
M3 - Conference contribution
C2 - 34891667
AN - SCOPUS:85122520789
T3 - Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
SP - 1942
EP - 1945
BT - 43rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2021
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 43rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2021
Y2 - 1 November 2021 through 5 November 2021
ER -