Identification Of Asthma Severity Levels Through Wheeze Sound Characterization And Classification Using Integrated Power Features

Nabi, Fizza Ghulam and Sundaraj, Kenneth and Chee, Kiang Lam (2019) Identification Of Asthma Severity Levels Through Wheeze Sound Characterization And Classification Using Integrated Power Features. Biomedical Signal Processing and Control, 52. 302 - 311. ISSN 1746-8094

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Abstract

This study aimed to investigate and classify wheeze sound characteristics according to asthma severity levels (mild, moderate and severe) using integrated power (IP) features. Method: Validated and segmented wheeze sounds were obtained from the lower lung base (LLB) and trachea recordings of 55 asthmatic patients with different severity levels during tidal breathing manoeuvres. From the segments, nine datasets were obtained based on the auscultation location, breath phases and their combination. In this study, IP features were extracted for assessing asthma severity. Subsequently, univariate and multivariate (MANOVA) statistical analyses were separately implemented to analyse behaviour of wheeze sounds according to severity levels. Furthermore, the ensemble (ENS), knearest- neighbour (KNN) and support vector machine (SVM) classifiers were applied to classify the asthma severity levels. Results and conclusion: The univariate results of this study indicated that the majority of features significantly discriminated (p < 0.05) the severity levels in all the datasets. The MANOVA results yielded significantly (p < 0.05) large effect size in all datasets (including LLB-related) and almost all post hoc results were significant(p < 0.05). A comparison ofthe performance of classifiers revealed that eight ofthe nine datasets showed improved performance with the ENS classifier. The Trachea inspiratory (T-Inspir) dataset produced the highest performance. The overall best positive predictive rate (PPR) for the mild, moderate and severe severity levels were 100% (KNN), 92% (SVM) and 94% (ENS) respectively. Analysis related to auscultation locations revealed that tracheal wheeze sounds are more specific and sensitive predictors of asthma severity. Additionally, phase related investigations indicated that expiratory and inspiratory wheeze sounds are equally informative for the classification of asthma severity

Item Type:	Article
Uncontrolled Keywords:	Asthma, Breath Sounds, Wheeze Classification, Airway Obstruction, Severity Level, Integrated Power, Wheeze Detection
Divisions:	Faculty of Electronics and Computer Engineering > Department of Industrial Electronics
Depositing User:	Norfaradilla Idayu Ab. Ghafar
Date Deposited:	22 Dec 2020 12:33
Last Modified:	22 Dec 2020 12:33
URI:	http://eprints.utem.edu.my/id/eprint/24363
Statistic Details:	View Download Statistic

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