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

• Autorzy: Ghulam Nabi Fizza , Sundaraj Kenneth , Iqbal Muhammad Shahid , Shafiq Muhammad , Palaniappan Rajkumar

Identyfikatory

DOI

10.1016/j.bbe.2022.11.001

• Języki publikacji: EN

Abstrakty

EN

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.

Słowa kluczowe

EN

asthma severity level; MFCC; telemedicine device; wheeze classification; wheeze detection

PL

stopień nasilenia astmy; MFCC; urządzenie telemedyczne; świszczący oddech

• Wydawca: Nałęcz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences ; Elsevier
• Czasopismo: Biocybernetics and Biomedical Engineering
• Rocznik: 2022
• Tom: Vol. 42, no. 4
• Strony: 1236--1247
• Opis fizyczny: Bibliogr. 42 poz., rys., tab., wykr.

Twórcy

autor

Ghulam Nabi Fizza

• Department of Industrial Engineering and Management, University of the Punjab, Lahore, Pakistan

autor

Sundaraj Kenneth

• Faculty of Electronics & Computer Engineering, Universiti Teknikal Malaysia Melaka, Malaysia

autor

Iqbal Muhammad Shahid

• Department of Electrical Engineering, National University of Technology, Islamabad, Pakistan

autor

Shafiq Muhammad

• Department of Industrial Engineering and Management, University of the Punjab, Lahore, Pakistan

autor

Palaniappan Rajkumar

• Department of Mechatronics Engineering, College of Engineering, University of Technology Bahrain, Salmabad Kingdom of Bahrain

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Uwagi

PL

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023)