Open AccessAcquisition and Classification of Lung Sounds for Improving the Efficacy of Auscultation Diagnosis of Pulmonary Diseases
Author(s) -
Biruk Abera Tessema,
Hundessa Daba Nemomssa,
Gizeaddis Lamesgin Simegn
Publication year - 2022
Publication title -
medical devices
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.482
H-Index - 30
ISSN - 1179-1470
DOI - 10.2147/mder.s362407
Subject(s) - auscultation , stethoscope , respiratory sounds , medicine , support vector machine , crackles , lung , computer science , speech recognition , pattern recognition (psychology) , artificial intelligence , asthma , machine learning , radiology
Lung diseases are the third leading cause of death worldwide. Stethoscope-based auscultation is the most commonly used, non-invasive, inexpensive, and primary diagnostic approach for assessing lung conditions. However, the manual auscultation-based diagnosis procedure is prone to error, and its accuracy is dependent on the physician's experience and hearing capacity. Moreover, the stethoscope recording is vulnerable to different noises that can mask the important features of lung sounds which may lead to misdiagnosis. In this paper, a method for the acquisition of lung sound signals and classification of the top 7 lung diseases has been proposed for improving the efficacy of auscultation diagnosis of pulmonary disease.