Classification Of Pulmonary Pathology From Breath Sounds Using The Wavelet Packet Transform And An Extreme Learning Machine

Sundaraj, Kenneth and Palaniappan, Rajkumar and Sundaraj, Sebastian and Huliraj, N. and Revadi, S. S. (2018) Classification Of Pulmonary Pathology From Breath Sounds Using The Wavelet Packet Transform And An Extreme Learning Machine. Biomedizinische Technik, 63. pp. 383-394. ISSN 1862-278X

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Abstract

Background:Auscultation is a medical procedure used for the initial diagnosis and assessment of lung and heart diseases.From this perspective,we propose assessing the performance of the extreme learning machine (ELM) classifiers for the diagnosis of pulmonary pathology using breath sounds.Methods:Energy and entropy features were extracted from the breath sound using the wavelet packet transform.The statistical significance of the extracted features was evaluated by one-way analysis of variance (ANOVA).The extracted features were inputted into the ELM classifier.Results:The maximum classification accuracies obtained for the conventional validation (CV) of the energy and entropy features were 97.36% and 98.37%,respectively,whereas the accuracies obtained for the cross validation (CRV) of the energy and entropy features were 96.80% and 97.91%,respectively.In addition,maximum classification accuracies of 98.25% and 99.25% were obtained for the CV and CRV of the ensemble features,respectively.Conclusion:The results indicate that the classification accuracy obtained with the ensemble features was higher than those obtained with the energy and entropy features.

Item Type:	Article
Uncontrolled Keywords:	breath sounds; extreme learning machine; health care; wavelet packet transform.
Subjects:	T Technology > T Technology (General) T Technology > TK Electrical engineering. Electronics Nuclear engineering
Divisions:	Faculty of Electronics and Computer Engineering
Depositing User:	Mohd. Nazir Taib
Date Deposited:	14 Aug 2019 03:56
Last Modified:	27 Aug 2021 12:04
URI:	http://eprints.utem.edu.my/id/eprint/22945
Statistic Details:	View Download Statistic

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