A telemedicine software application for asthma severity levels identification using wheeze sounds classification

Nabi, Fizza Ghulam and Shafiq, Muhammad and Sundaraj, Kenneth and Iqbal, Muhammad Shahid and Palaniappan, Rajkumar (2022) A telemedicine software application for asthma severity levels identification using wheeze sounds classification. Biocybernetics and Biomedical Engineering, 42 (4). pp. 1236-1247. ISSN 2391-467X

Text 2022 FIZZA BBE.PDF Restricted to Registered users only Download (2MB)

Abstract

Early and precise knowledge of asthma severity levels may help in effective precautions, proper medication, and follow-up planning for the patients. Keeping this in view, we propose a telemedicine application that is capable of automatically identifying the severity level of asthma patients by using machine learning techniques. Respiratory sounds of 111 asthmatic patients were collected. The 111-patient dataset consisted of 34 mild, 36 moderate, and 41 severe levels. Data was collected from two auscultation locations, i.e., from the trachea and lower lung base. The first dataset was used for the testing and training (cross-validation) of classifiers while a second database was used for the validation of the system. Mel-frequency cepstral coefficient (MFCC) features were extracted to discriminate the severity levels. Then, ensemble and k-nearest neighbor (KNN) classifiers were used for classification. This was performed on both auscultation locations jointly and individually. The developed telemedicine application, based on MFCC features and classifiers, automatically detects wheeze and classifies it into a severity level. The extracted features showed significant differences (p < 0.05) for all severity levels. Based on the testing, training, and validation results, the performance of the ensemble and KNN classifiers were comparable. MFCC-based features classification provides maximum accuracy of 99%, 90%, and 89% for mild, moderate, and severe samples, respectively. The average rate of wheeze detection was observed to be 93%. The maximum accuracy of validation of the telemedicine application was found to be 57%, 72%, and 76% for mild, moderate, and severe levels, respectively.

Item Type:	Article
Uncontrolled Keywords:	Asthma severity level, MFCC, Telemedicine device, Wheeze classification, Wheeze detection
Divisions:	Faculty of Electronics and Computer Engineering
Depositing User:	mr eiisaa ahyead
Date Deposited:	24 Mar 2023 10:55
Last Modified:	24 Mar 2023 10:55
URI:	http://eprints.utem.edu.my/id/eprint/26599
Statistic Details:	View Download Statistic

Actions (login required)

View Item