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Characterization And Classification Of Asthmatic Wheeze Sounds According To Severity Level Using Spectral Integrated Features

This study aimed to investigate and classify wheeze sounds of asthmatic patients according to their severity level (mild, moderate and severe) using spectral integrated (SI) features. Method: Segmented and validated wheeze sounds were obtained from auscultation recordings of the trachea and lower lu...

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Main Authors: Nabi, Fizza Ghulam, Sundaraj, Kenneth, Chee, Kiang Lam, Palaniappan, Rajkumar
Format: Article
Language:English
Published: Elsevier Ltd. 2019
Online Access:http://eprints.utem.edu.my/id/eprint/24359/2/2019%20FIZZA%20CBM.PDF
http://eprints.utem.edu.my/id/eprint/24359/
https://www.sciencedirect.com/science/article/pii/S0010482518303378
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spelling my.utem.eprints.243592020-12-22T12:32:17Z http://eprints.utem.edu.my/id/eprint/24359/ Characterization And Classification Of Asthmatic Wheeze Sounds According To Severity Level Using Spectral Integrated Features Nabi, Fizza Ghulam Sundaraj, Kenneth Chee, Kiang Lam Palaniappan, Rajkumar This study aimed to investigate and classify wheeze sounds of asthmatic patients according to their severity level (mild, moderate and severe) using spectral integrated (SI) features. Method: Segmented and validated wheeze sounds were obtained from auscultation recordings of the trachea and lower lung base of 55 asthmatic patients during tidal breathing manoeuvres. The segments were multi-labelled into 9 groups based on the auscultation location and/or breath phases. Bandwidths were selected based on the physiology, and a corresponding SI feature was computed for each segment. Univariate and multivariate statistical analyses were then performed to investigate the discriminatory behaviour of the features with respect to the severity levels in the various groups. The asthmatic severity levels in the groups were then classified using the ensemble (ENS), support vector machine (SVM) and k-nearest neighbour (KNN) methods. Results and conclusion: All statistical comparisons exhibited a significant difference (p < 0.05) among the severity levels with few exceptions. In the classification experiments, the ensemble classifier exhibited better performance in terms of sensitivity, specificity and positive predictive value (PPV). The trachea inspiratory group showed the highest classification performance compared with all the other groups. Overall, the best PPV for the mild, moderate and severe samples were 95% (ENS), 88% (ENS) and 90% (SVM), respectively. With respect to location, the tracheal related wheeze sounds were most sensitive and specific predictors of asthma severity levels. In addition, the classification performances of the inspiratory and expiratory related groups were comparable, suggesting that the samples from these locations are equally informative Elsevier Ltd. 2019-01 Article PeerReviewed text en http://eprints.utem.edu.my/id/eprint/24359/2/2019%20FIZZA%20CBM.PDF Nabi, Fizza Ghulam and Sundaraj, Kenneth and Chee, Kiang Lam and Palaniappan, Rajkumar (2019) Characterization And Classification Of Asthmatic Wheeze Sounds According To Severity Level Using Spectral Integrated Features. Computers in Biology and Medicine, 104. 52 - 61. ISSN 0010-4825 https://www.sciencedirect.com/science/article/pii/S0010482518303378 10.1016/j.compbiomed.2018.10.035
institution Universiti Teknikal Malaysia Melaka
building UTEM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknikal Malaysia Melaka
content_source UTEM Institutional Repository
url_provider http://eprints.utem.edu.my/
language English
description This study aimed to investigate and classify wheeze sounds of asthmatic patients according to their severity level (mild, moderate and severe) using spectral integrated (SI) features. Method: Segmented and validated wheeze sounds were obtained from auscultation recordings of the trachea and lower lung base of 55 asthmatic patients during tidal breathing manoeuvres. The segments were multi-labelled into 9 groups based on the auscultation location and/or breath phases. Bandwidths were selected based on the physiology, and a corresponding SI feature was computed for each segment. Univariate and multivariate statistical analyses were then performed to investigate the discriminatory behaviour of the features with respect to the severity levels in the various groups. The asthmatic severity levels in the groups were then classified using the ensemble (ENS), support vector machine (SVM) and k-nearest neighbour (KNN) methods. Results and conclusion: All statistical comparisons exhibited a significant difference (p < 0.05) among the severity levels with few exceptions. In the classification experiments, the ensemble classifier exhibited better performance in terms of sensitivity, specificity and positive predictive value (PPV). The trachea inspiratory group showed the highest classification performance compared with all the other groups. Overall, the best PPV for the mild, moderate and severe samples were 95% (ENS), 88% (ENS) and 90% (SVM), respectively. With respect to location, the tracheal related wheeze sounds were most sensitive and specific predictors of asthma severity levels. In addition, the classification performances of the inspiratory and expiratory related groups were comparable, suggesting that the samples from these locations are equally informative
format Article
author Nabi, Fizza Ghulam
Sundaraj, Kenneth
Chee, Kiang Lam
Palaniappan, Rajkumar
spellingShingle Nabi, Fizza Ghulam
Sundaraj, Kenneth
Chee, Kiang Lam
Palaniappan, Rajkumar
Characterization And Classification Of Asthmatic Wheeze Sounds According To Severity Level Using Spectral Integrated Features
author_facet Nabi, Fizza Ghulam
Sundaraj, Kenneth
Chee, Kiang Lam
Palaniappan, Rajkumar
author_sort Nabi, Fizza Ghulam
title Characterization And Classification Of Asthmatic Wheeze Sounds According To Severity Level Using Spectral Integrated Features
title_short Characterization And Classification Of Asthmatic Wheeze Sounds According To Severity Level Using Spectral Integrated Features
title_full Characterization And Classification Of Asthmatic Wheeze Sounds According To Severity Level Using Spectral Integrated Features
title_fullStr Characterization And Classification Of Asthmatic Wheeze Sounds According To Severity Level Using Spectral Integrated Features
title_full_unstemmed Characterization And Classification Of Asthmatic Wheeze Sounds According To Severity Level Using Spectral Integrated Features
title_sort characterization and classification of asthmatic wheeze sounds according to severity level using spectral integrated features
publisher Elsevier Ltd.
publishDate 2019
url http://eprints.utem.edu.my/id/eprint/24359/2/2019%20FIZZA%20CBM.PDF
http://eprints.utem.edu.my/id/eprint/24359/
https://www.sciencedirect.com/science/article/pii/S0010482518303378
_version_ 1687397266461032448
score 13.211869

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