Characterization of ventricular tachycardia and ventricular fibrillation using semantic mining

Ventricular tachycardia and ventricular fibrillation are ventricular cardiac arrhythmia that could be calamitous and life threatening. The ability to provide accurate predictions of ventricular tachycardia or ventricular fibrillation events can save lives. The purpose of this work was to investigate...

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Main Author:	Mohammed Sheet, Sinan S.
Format:	Thesis
Language:	English
Published:	2011
Subjects:	TK Electrical engineering Electronics Nuclear engineering
Online Access:	http://eprints.utm.my/id/eprint/31933/5/SinanSMohammedSheetMFKE2011.pdf
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id	my-utm-ep.31933
record_format	uketd_dc
spelling	my-utm-ep.319332018-05-27T07:10:42Z Characterization of ventricular tachycardia and ventricular fibrillation using semantic mining 2011-05 Mohammed Sheet, Sinan S. TK Electrical engineering. Electronics Nuclear engineering Ventricular tachycardia and ventricular fibrillation are ventricular cardiac arrhythmia that could be calamitous and life threatening. The ability to provide accurate predictions of ventricular tachycardia or ventricular fibrillation events can save lives. The purpose of this work was to investigate the possibility of using a semantic mining algorithm to predict the onset of ventricular tachycardia and ventricular fibrillation in electrocardiogram (ECG) signals. A total of eighteen subjects were obtained from Creighton University Ventricular Tachyarrhythmia Database and MIT-BIH Arrhythmia Database. Normal patient ventricular tachycardia, ventricular tachycardia, ventricular fibrillation for the same subject were classified based on annotations supplied by specialists at the Creighton University Cardiac Center. Based on these downloaded data damping ratios, natural frequencies and input parameters were developed using Semantic mining algorithm. The average value of the three developed parameters was determined. These average values were tabulated in sequence with time. Based on true observation, three ratios were taken: first, between the derivative of input parameter and natural frequency; second, between the input parameter and damping ratio; and third, between natural frequency and damping ratio. These ratios are characterized as new parameters and depending on the maximum amplitude for these new parameters, a threshold value is selected to predict the onset of ventricular tachycardia and ventricular fibrillation. In this study, it was found that the new parameters had different amplitude patterns with time according to conditions for the same subject, and the same patterns emerged for the same condition among different subjects. In addition, applying the selected threshold achieved successful result was one to four minute in the forecasting the onset of both Ventricular tachycardia and ventricular fibrillation. In brief this work provides a new method for advanced researches in distinguish and predict of heart disease. 2011-05 Thesis http://eprints.utm.my/id/eprint/31933/ http://eprints.utm.my/id/eprint/31933/5/SinanSMohammedSheetMFKE2011.pdf application/pdf en public masters 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 Mohammed Sheet, Sinan S. Characterization of ventricular tachycardia and ventricular fibrillation using semantic mining
description	Ventricular tachycardia and ventricular fibrillation are ventricular cardiac arrhythmia that could be calamitous and life threatening. The ability to provide accurate predictions of ventricular tachycardia or ventricular fibrillation events can save lives. The purpose of this work was to investigate the possibility of using a semantic mining algorithm to predict the onset of ventricular tachycardia and ventricular fibrillation in electrocardiogram (ECG) signals. A total of eighteen subjects were obtained from Creighton University Ventricular Tachyarrhythmia Database and MIT-BIH Arrhythmia Database. Normal patient ventricular tachycardia, ventricular tachycardia, ventricular fibrillation for the same subject were classified based on annotations supplied by specialists at the Creighton University Cardiac Center. Based on these downloaded data damping ratios, natural frequencies and input parameters were developed using Semantic mining algorithm. The average value of the three developed parameters was determined. These average values were tabulated in sequence with time. Based on true observation, three ratios were taken: first, between the derivative of input parameter and natural frequency; second, between the input parameter and damping ratio; and third, between natural frequency and damping ratio. These ratios are characterized as new parameters and depending on the maximum amplitude for these new parameters, a threshold value is selected to predict the onset of ventricular tachycardia and ventricular fibrillation. In this study, it was found that the new parameters had different amplitude patterns with time according to conditions for the same subject, and the same patterns emerged for the same condition among different subjects. In addition, applying the selected threshold achieved successful result was one to four minute in the forecasting the onset of both Ventricular tachycardia and ventricular fibrillation. In brief this work provides a new method for advanced researches in distinguish and predict of heart disease.
format	Thesis
qualification_level	Master's degree
author	Mohammed Sheet, Sinan S.
author_facet	Mohammed Sheet, Sinan S.
author_sort	Mohammed Sheet, Sinan S.
title	Characterization of ventricular tachycardia and ventricular fibrillation using semantic mining
title_short	Characterization of ventricular tachycardia and ventricular fibrillation using semantic mining
title_full	Characterization of ventricular tachycardia and ventricular fibrillation using semantic mining
title_fullStr	Characterization of ventricular tachycardia and ventricular fibrillation using semantic mining
title_full_unstemmed	Characterization of ventricular tachycardia and ventricular fibrillation using semantic mining
title_sort	characterization of ventricular tachycardia and ventricular fibrillation using semantic mining
granting_institution	Universiti Teknologi Malaysia, Faculty of Electrical Engineering
granting_department	Faculty of Electrical Engineering
publishDate	2011
url	http://eprints.utm.my/id/eprint/31933/5/SinanSMohammedSheetMFKE2011.pdf
_version_	1747815880767569920

Characterization of ventricular tachycardia and ventricular fibrillation using semantic mining

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