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John Amose
Karunya Institute of Technology and Sciences

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Optimal chest position of auscultation for chronic obstructive pulmonary disease diagnosis using machine learning John Amose; Manimegalai Vairavan
Indonesian Journal of Electrical Engineering and Computer Science Vol 32, No 3: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v32.i3.pp1417-1425

Abstract

Digital Stethoscopes over recent years have gained acceptance among pulmonologists to perform auscultations due to their advantages over traditional stethoscopes. During the previous decade, researchers have prominently contributed to the development of algorithms aimed at enabling objective diagnosis of respiratory sounds and conditions, thereby affording individuals lacking medical expertise the capability to auscultate themselves. However, auscultation requires the personnel to be aware of the optimal chest position to place the device for a reliable diagnosis as well. This study aims to identify the optimal chest position to place a digital stethoscope's diaphragm to objectively diagnose Chronic Obstructive Pulmonary Disease (COPD). Lung sound recordings from seven chest positions with data available in the ICBHI 2017 database namely, Anterior left (Al), Anterior right (Ar), Lateral left (Ll), Lateral right (Lr), Posterior left (Pl), Posterior right (Pr) and Trachea (Tc), were analyzed in this study. COPD+ and COPD- at diagnosis, each chest position was done objectively using Mel-Frequency Cepstral Coefficients (MFCC) features and machine learning models namely Support Vector Machine and Decision Tree. The results indicate that the Posterior right (Pr) chest position offers superior precision, recall, and F1 score, with a recognition accuracy of 99.7% in COPD screening.
Co-Authors Manimegalai Vairavan