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

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

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Abstract

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

Item Type:	Article
Uncontrolled Keywords:	Asthma, Breath Sounds, Wheeze Classification, Airway Obstruction, Severity Level
Divisions:	Faculty of Electronics and Computer Engineering > Department of Industrial Electronics
Depositing User:	Norfaradilla Idayu Ab. Ghafar
Date Deposited:	22 Dec 2020 12:32
Last Modified:	22 Dec 2020 12:32
URI:	http://eprints.utem.edu.my/id/eprint/24359
Statistic Details:	View Download Statistic

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