Framework for machine learning artefact removal and empirical mode decomposition for capnogram based asthma detection

Capnography has received considerable attention owing to its important applications in assessing asthma and other pulmonary diseases. Monitoring abnormal changes in the recorded carbon dioxide waveform (i.e., capnogram signal) allows for detecting respiratory malfunctioning and thereby averting pote...

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Main Author:	Elbadawy, Ismail Mohamed Ibrahim Bayoumy
Format:	Thesis
Language:	English
Published:	2023
Subjects:	TK Electrical engineering Electronics Nuclear engineering
Online Access:	http://eprints.utm.my/102785/1/IsmailMohamedIbrahimPSKE2023.pdf.pdf
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spelling	my-utm-ep.1027852023-09-20T04:08:28Z Framework for machine learning artefact removal and empirical mode decomposition for capnogram based asthma detection 2023 Elbadawy, Ismail Mohamed Ibrahim Bayoumy TK Electrical engineering. Electronics Nuclear engineering Capnography has received considerable attention owing to its important applications in assessing asthma and other pulmonary diseases. Monitoring abnormal changes in the recorded carbon dioxide waveform (i.e., capnogram signal) allows for detecting respiratory malfunctioning and thereby averting potential asthma attacks. Detecting asthma based on the non-stationary capnogram signal remains an open research problem. In this thesis, an automatic computational framework is proposed to detect asthma. The presented framework includes two main stages. The first stage is responsible for discarding the distorted segments of the recorded capnogram signals. This task was performed in previous studies either manually by visual inspection, using threshold-based or template matching methods. In the current work, a machine learning-based approach is presented to automatically classify artefact-free and distorted capnogram segments. For this purpose, different time- and frequencydomain features are proposed. The time-domain features include energy, variance, skewness, kurtosis, Hjorth parameters and mean absolute deviation (MAD). The frequency-domain features include the area under the magnitude Fourier spectrum in addition to the number of relatively high spectral peaks for a particular frequency range. Different classifiers are trained and tested using the most relevant features: Hjorth activity and MAD. These classification models include Support Vector Machine (SVM), Random Forest (RF), Decision Tree (DT) andNaive Bayes (NB) classifiers. The results showed that the SVM classifier can provide classification accuracy, specificity, sensitivity and precision of 89%, 91%, 87% and 92.1%, respectively. In addition, a multiple classifiers voting approach is proposed for this classification task. Using this cooperative classification, the specificity is increased from 91% to 94%. The second stage accepts the clean capnogram segments from the first stage and carries out the classification of healthy and asthmatic capnograms. The proposed features are based on Empirical Mode Decomposition (EMD) which is suitable for analyzing the non-stationary capnogram signal in addition to the variance of the raw signal. Unlike the traditional features, the proposed features are extracted from the frequency-domain representation of the signal’s first Intrinsic Mode Function (IMF). The results showed that the NB classifier can provide classification accuracy, specificity, sensitivity and precision of 96.5%, 97%, 96% and 97.18%, respectively. 2023 Thesis http://eprints.utm.my/102785/ http://eprints.utm.my/102785/1/IsmailMohamedIbrahimPSKE2023.pdf.pdf application/pdf en public http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:152230 phd doctoral Universiti Teknologi Malaysia Faculty of Engineering - School of Electrical Engineering
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
language	English
topic	TK Electrical engineering Electronics Nuclear engineering
spellingShingle	TK Electrical engineering Electronics Nuclear engineering Elbadawy, Ismail Mohamed Ibrahim Bayoumy Framework for machine learning artefact removal and empirical mode decomposition for capnogram based asthma detection
description	Capnography has received considerable attention owing to its important applications in assessing asthma and other pulmonary diseases. Monitoring abnormal changes in the recorded carbon dioxide waveform (i.e., capnogram signal) allows for detecting respiratory malfunctioning and thereby averting potential asthma attacks. Detecting asthma based on the non-stationary capnogram signal remains an open research problem. In this thesis, an automatic computational framework is proposed to detect asthma. The presented framework includes two main stages. The first stage is responsible for discarding the distorted segments of the recorded capnogram signals. This task was performed in previous studies either manually by visual inspection, using threshold-based or template matching methods. In the current work, a machine learning-based approach is presented to automatically classify artefact-free and distorted capnogram segments. For this purpose, different time- and frequencydomain features are proposed. The time-domain features include energy, variance, skewness, kurtosis, Hjorth parameters and mean absolute deviation (MAD). The frequency-domain features include the area under the magnitude Fourier spectrum in addition to the number of relatively high spectral peaks for a particular frequency range. Different classifiers are trained and tested using the most relevant features: Hjorth activity and MAD. These classification models include Support Vector Machine (SVM), Random Forest (RF), Decision Tree (DT) andNaive Bayes (NB) classifiers. The results showed that the SVM classifier can provide classification accuracy, specificity, sensitivity and precision of 89%, 91%, 87% and 92.1%, respectively. In addition, a multiple classifiers voting approach is proposed for this classification task. Using this cooperative classification, the specificity is increased from 91% to 94%. The second stage accepts the clean capnogram segments from the first stage and carries out the classification of healthy and asthmatic capnograms. The proposed features are based on Empirical Mode Decomposition (EMD) which is suitable for analyzing the non-stationary capnogram signal in addition to the variance of the raw signal. Unlike the traditional features, the proposed features are extracted from the frequency-domain representation of the signal’s first Intrinsic Mode Function (IMF). The results showed that the NB classifier can provide classification accuracy, specificity, sensitivity and precision of 96.5%, 97%, 96% and 97.18%, respectively.
format	Thesis
qualification_name	Doctor of Philosophy (PhD.)
qualification_level	Doctorate
author	Elbadawy, Ismail Mohamed Ibrahim Bayoumy
author_facet	Elbadawy, Ismail Mohamed Ibrahim Bayoumy
author_sort	Elbadawy, Ismail Mohamed Ibrahim Bayoumy
title	Framework for machine learning artefact removal and empirical mode decomposition for capnogram based asthma detection
title_short	Framework for machine learning artefact removal and empirical mode decomposition for capnogram based asthma detection
title_full	Framework for machine learning artefact removal and empirical mode decomposition for capnogram based asthma detection
title_fullStr	Framework for machine learning artefact removal and empirical mode decomposition for capnogram based asthma detection
title_full_unstemmed	Framework for machine learning artefact removal and empirical mode decomposition for capnogram based asthma detection
title_sort	framework for machine learning artefact removal and empirical mode decomposition for capnogram based asthma detection
granting_institution	Universiti Teknologi Malaysia
granting_department	Faculty of Engineering - School of Electrical Engineering
publishDate	2023
url	http://eprints.utm.my/102785/1/IsmailMohamedIbrahimPSKE2023.pdf.pdf
_version_	1783729217077248000

Framework for machine learning artefact removal and empirical mode decomposition for capnogram based asthma detection

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