An optimization method based on genetic algorithm for heart rate variability analysis in the prediction of the onset of cardiac arrhythmia

Heart rate variability (HRV) is one of the common biological markers for developing a diagnostic system of cardiovascular disease. HRV analysis is used to extract statistical, geometrical, spectral and non-linear features in such diagnostic system. The diagnostic accuracy can be maximized by applyin...

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Main Author:	Boon, Khang Hua
Format:	Thesis
Language:	English
Published:	2017
Subjects:	TK Electrical engineering Electronics Nuclear engineering
Online Access:	http://eprints.utm.my/id/eprint/84006/1/BoonKhangHuaPFKE2017.pdf
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spelling	my-utm-ep.840062019-11-05T04:33:52Z An optimization method based on genetic algorithm for heart rate variability analysis in the prediction of the onset of cardiac arrhythmia 2017-06 Boon, Khang Hua TK Electrical engineering. Electronics Nuclear engineering Heart rate variability (HRV) is one of the common biological markers for developing a diagnostic system of cardiovascular disease. HRV analysis is used to extract statistical, geometrical, spectral and non-linear features in such diagnostic system. The diagnostic accuracy can be maximized by applying a feature selection step that selects an optimal feature subset from the extracted features. However, there are shortcomings in using only the feature selection for optimizing a diagnostic system that is based on HRV analysis. One of the main limitations is that the parameters of HRV feature extraction algorithms are not optimized for maximal performance. In addition, the feature selection process does not consider the feature cost and misclassification error of the selected optimal feature subset. Therefore, this thesis proposes a multi-objective optimization method that is based on the non-dominated sorting genetic algorithm to overcome these shortcomings in a cardiac arrhythmia prediction system. It optimizes the HRV feature extraction parameters, selects the best feature subset, and tunes the classifier parameters simultaneously for maximum prediction performance. The proposed optimization algorithm is applied in two cardiac arrhythmia cases, namely the prediction of the onsets of paroxysmal atrial fibrillation (PAF) and ventricular tachyarrhythmia (VTA). In the proposed approach, trade-off between multiple optimization objectives that contradict to each other are also analyzed. The optimization objectives include the feature count, measurement cost, prediction sensitivity, specificity and accuracy rate. The following results prove the effectiveness of the proposed optimization algorithm in the two arrhythmia cases. Firstly, the PAF onset prediction achieves an accuracy rate of 89.6%, which significantly outperforms most of the previous works. This accuracy rate is achieved even with the HRV signal length being reduced from the typical 30 minutes to just 5 minutes (a reduction of 83%). In the case of VTA onset prediction, the accuracy rate of 78.15% is achieved with 5-minute signal length. This result outperforms previous works. Another significant result is the sensitivity rate improvement with the tradeoff of lower specificity and accuracy rate for both PAF and VTA onset predictions. For instance, the sensitivity rate of the VTA onset prediction system improved from 81.48% to 92.59% while the accuracy rate reduced from 78.15% to 72.59%. 2017-06 Thesis http://eprints.utm.my/id/eprint/84006/ http://eprints.utm.my/id/eprint/84006/1/BoonKhangHuaPFKE2017.pdf application/pdf en public http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:126157 phd doctoral Universiti Teknologi Malaysia, Faculty of Electrical Engineering Faculty 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 Boon, Khang Hua An optimization method based on genetic algorithm for heart rate variability analysis in the prediction of the onset of cardiac arrhythmia
description	Heart rate variability (HRV) is one of the common biological markers for developing a diagnostic system of cardiovascular disease. HRV analysis is used to extract statistical, geometrical, spectral and non-linear features in such diagnostic system. The diagnostic accuracy can be maximized by applying a feature selection step that selects an optimal feature subset from the extracted features. However, there are shortcomings in using only the feature selection for optimizing a diagnostic system that is based on HRV analysis. One of the main limitations is that the parameters of HRV feature extraction algorithms are not optimized for maximal performance. In addition, the feature selection process does not consider the feature cost and misclassification error of the selected optimal feature subset. Therefore, this thesis proposes a multi-objective optimization method that is based on the non-dominated sorting genetic algorithm to overcome these shortcomings in a cardiac arrhythmia prediction system. It optimizes the HRV feature extraction parameters, selects the best feature subset, and tunes the classifier parameters simultaneously for maximum prediction performance. The proposed optimization algorithm is applied in two cardiac arrhythmia cases, namely the prediction of the onsets of paroxysmal atrial fibrillation (PAF) and ventricular tachyarrhythmia (VTA). In the proposed approach, trade-off between multiple optimization objectives that contradict to each other are also analyzed. The optimization objectives include the feature count, measurement cost, prediction sensitivity, specificity and accuracy rate. The following results prove the effectiveness of the proposed optimization algorithm in the two arrhythmia cases. Firstly, the PAF onset prediction achieves an accuracy rate of 89.6%, which significantly outperforms most of the previous works. This accuracy rate is achieved even with the HRV signal length being reduced from the typical 30 minutes to just 5 minutes (a reduction of 83%). In the case of VTA onset prediction, the accuracy rate of 78.15% is achieved with 5-minute signal length. This result outperforms previous works. Another significant result is the sensitivity rate improvement with the tradeoff of lower specificity and accuracy rate for both PAF and VTA onset predictions. For instance, the sensitivity rate of the VTA onset prediction system improved from 81.48% to 92.59% while the accuracy rate reduced from 78.15% to 72.59%.
format	Thesis
qualification_name	Doctor of Philosophy (PhD.)
qualification_level	Doctorate
author	Boon, Khang Hua
author_facet	Boon, Khang Hua
author_sort	Boon, Khang Hua
title	An optimization method based on genetic algorithm for heart rate variability analysis in the prediction of the onset of cardiac arrhythmia
title_short	An optimization method based on genetic algorithm for heart rate variability analysis in the prediction of the onset of cardiac arrhythmia
title_full	An optimization method based on genetic algorithm for heart rate variability analysis in the prediction of the onset of cardiac arrhythmia
title_fullStr	An optimization method based on genetic algorithm for heart rate variability analysis in the prediction of the onset of cardiac arrhythmia
title_full_unstemmed	An optimization method based on genetic algorithm for heart rate variability analysis in the prediction of the onset of cardiac arrhythmia
title_sort	optimization method based on genetic algorithm for heart rate variability analysis in the prediction of the onset of cardiac arrhythmia
granting_institution	Universiti Teknologi Malaysia, Faculty of Electrical Engineering
granting_department	Faculty of Electrical Engineering
publishDate	2017
url	http://eprints.utm.my/id/eprint/84006/1/BoonKhangHuaPFKE2017.pdf
_version_	1747818424579391488

An optimization method based on genetic algorithm for heart rate variability analysis in the prediction of the onset of cardiac arrhythmia

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