A performance comparison of wheeze feature extraction methods for asthma severity levels classification

Asthma is a chronic disease that requires monitoring and treatment throughout the patient's lifetime. The common adventitious sounds related to asthma are wheezes. A study that has classified the severity of asthma using wheezes are still lacking in the field, therefore, the purpose of this wor...

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Main Authors:	Syamimi Mardiah, Shaharum, Sundaraj, Kenneth, Shazmin, Aniza, Palaniappan, Rajkumar, Helmy, Khaled
Format:	Conference or Workshop Item
Language:	English
Published:	IEEE 2019
Subjects:	TA Engineering (General). Civil engineering (General) TK Electrical engineering. Electronics Nuclear engineering
Online Access:	http://umpir.ump.edu.my/id/eprint/25686/ http://umpir.ump.edu.my/id/eprint/25686/ http://umpir.ump.edu.my/id/eprint/25686/1/A%20performance%20comparison%20of%20wheeze%20feature%20extraction%20methods%20.pdf

id	ump-25686
recordtype	eprints
spelling	ump-256862019-12-17T02:43:01Z http://umpir.ump.edu.my/id/eprint/25686/ A performance comparison of wheeze feature extraction methods for asthma severity levels classification Syamimi Mardiah, Shaharum Sundaraj, Kenneth Shazmin, Aniza Palaniappan, Rajkumar Helmy, Khaled TA Engineering (General). Civil engineering (General) TK Electrical engineering. Electronics Nuclear engineering Asthma is a chronic disease that requires monitoring and treatment throughout the patient's lifetime. The common adventitious sounds related to asthma are wheezes. A study that has classified the severity of asthma using wheezes are still lacking in the field, therefore, the purpose of this work is to compare feature extraction methods for the classification of asthma severity level. Three types of features opted are mel frequency cepstral coefficients (MFCC); short time energy (STE); auto-regressive model and k-nearest neighbor (KNN) classifier is used in representing the performance of the feature used. Based on the overall performance between the features, MFCC features and KNN classifier shows the best and the highest performance with 95.92%, 96.33% and 98.42% average accuracy, sensitivity and specificity value obtained compared to STE that only obtained the highest average accuracy, sensitivity and specificity value of 84.94%, 87.33% and 95% respectively while AR features only obtained the highest average accuracy, sensitivity and specificity value of 49.43%, 52.17%, and 82.79% respectively. IEEE 2019-03-01 Conference or Workshop Item PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/25686/1/A%20performance%20comparison%20of%20wheeze%20feature%20extraction%20methods%20.pdf Syamimi Mardiah, Shaharum and Sundaraj, Kenneth and Shazmin, Aniza and Palaniappan, Rajkumar and Helmy, Khaled (2019) A performance comparison of wheeze feature extraction methods for asthma severity levels classification. In: 9th IEEE Control And System Graduate Research Colloquium (ICSGRC 2018), 3-4 August 2018 , Shah Alam, Selangor. pp. 145-150. (8657630). ISBN 978-1-5386-6321-9 https://doi.org/10.1109/ICSGRC.2018.8657630
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Malaysia Pahang
building	UMP Institutional Repository
collection	Online Access
language	English
topic	TA Engineering (General). Civil engineering (General) TK Electrical engineering. Electronics Nuclear engineering
spellingShingle	TA Engineering (General). Civil engineering (General) TK Electrical engineering. Electronics Nuclear engineering Syamimi Mardiah, Shaharum Sundaraj, Kenneth Shazmin, Aniza Palaniappan, Rajkumar Helmy, Khaled A performance comparison of wheeze feature extraction methods for asthma severity levels classification
description	Asthma is a chronic disease that requires monitoring and treatment throughout the patient's lifetime. The common adventitious sounds related to asthma are wheezes. A study that has classified the severity of asthma using wheezes are still lacking in the field, therefore, the purpose of this work is to compare feature extraction methods for the classification of asthma severity level. Three types of features opted are mel frequency cepstral coefficients (MFCC); short time energy (STE); auto-regressive model and k-nearest neighbor (KNN) classifier is used in representing the performance of the feature used. Based on the overall performance between the features, MFCC features and KNN classifier shows the best and the highest performance with 95.92%, 96.33% and 98.42% average accuracy, sensitivity and specificity value obtained compared to STE that only obtained the highest average accuracy, sensitivity and specificity value of 84.94%, 87.33% and 95% respectively while AR features only obtained the highest average accuracy, sensitivity and specificity value of 49.43%, 52.17%, and 82.79% respectively.
format	Conference or Workshop Item
author	Syamimi Mardiah, Shaharum Sundaraj, Kenneth Shazmin, Aniza Palaniappan, Rajkumar Helmy, Khaled
author_facet	Syamimi Mardiah, Shaharum Sundaraj, Kenneth Shazmin, Aniza Palaniappan, Rajkumar Helmy, Khaled
author_sort	Syamimi Mardiah, Shaharum
title	A performance comparison of wheeze feature extraction methods for asthma severity levels classification
title_short	A performance comparison of wheeze feature extraction methods for asthma severity levels classification
title_full	A performance comparison of wheeze feature extraction methods for asthma severity levels classification
title_fullStr	A performance comparison of wheeze feature extraction methods for asthma severity levels classification
title_full_unstemmed	A performance comparison of wheeze feature extraction methods for asthma severity levels classification
title_sort	performance comparison of wheeze feature extraction methods for asthma severity levels classification
publisher	IEEE
publishDate	2019
url	http://umpir.ump.edu.my/id/eprint/25686/ http://umpir.ump.edu.my/id/eprint/25686/ http://umpir.ump.edu.my/id/eprint/25686/1/A%20performance%20comparison%20of%20wheeze%20feature%20extraction%20methods%20.pdf
first_indexed	2023-09-18T22:39:35Z
last_indexed	2023-09-18T22:39:35Z
_version_	1777416825182617600

A performance comparison of wheeze feature extraction methods for asthma severity levels classification

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