Arrhythmia heart disease classification using deep learning

Arrhythmia affects millions of people in the world. Sudden cardiac death is the cause about half of deaths due to cardiovascular disease and about 15% of all deaths globally. About 80% of sudden cardiac death is the result of ventricular arrhythmias. Arrhythmias may occur at any age but are more co...

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Main Author:	Abdulkarim Farah, Abdulkhaliq
Format:	Thesis
Language:	English English English
Published:	2020
Subjects:	RC Internal medicine
Online Access:	http://eprints.uthm.edu.my/375/1/24p%20ABDULKHALIQ%20ABDULKARIM%20FARAH.pdf http://eprints.uthm.edu.my/375/2/ABDULKHALIQ%20ABDULKARIM%20FARAH%20COPYRIGHT%20DECLARATION.pdf http://eprints.uthm.edu.my/375/3/ABDULKHALIQ%20ABDULKARIM%20FARAH%20WATERMARK.pdf
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id	my-uthm-ep.375
record_format	uketd_dc
spelling	my-uthm-ep.3752021-07-25T01:12:03Z Arrhythmia heart disease classification using deep learning 2020-01 Abdulkarim Farah, Abdulkhaliq RC Internal medicine Arrhythmia affects millions of people in the world. Sudden cardiac death is the cause about half of deaths due to cardiovascular disease and about 15% of all deaths globally. About 80% of sudden cardiac death is the result of ventricular arrhythmias. Arrhythmias may occur at any age but are more common among older people. Arrhythmias are caused by problems with the electrical conduction system of the heart. Therefore, we have designed a model using supervised deep learning to classify the heartbeats extracted from an ECG into four (4) heartbeat classes which is normal beat, ventricular ectopic beat (VEB), supraventricular ectopic beat (SVEB) and fusion beat, based only on the line shape (morphology) of the individual heartbeats. The overall performance of the system resulted in a precision of 95.378%, a recall of 81.3035%, accuracy of 97.62% and an F1 score 84.6875%. 2020-01 Thesis http://eprints.uthm.edu.my/375/ http://eprints.uthm.edu.my/375/1/24p%20ABDULKHALIQ%20ABDULKARIM%20FARAH.pdf text en public http://eprints.uthm.edu.my/375/2/ABDULKHALIQ%20ABDULKARIM%20FARAH%20COPYRIGHT%20DECLARATION.pdf text en staffonly http://eprints.uthm.edu.my/375/3/ABDULKHALIQ%20ABDULKARIM%20FARAH%20WATERMARK.pdf text en validuser mphil masters Universiti Tun Hussein Onn Malaysia Fakulti Kejuruteraan Elektrik dan Elektronik
institution	Universiti Tun Hussein Onn Malaysia
collection	UTHM Institutional Repository
language	English English English
topic	RC Internal medicine
spellingShingle	RC Internal medicine Abdulkarim Farah, Abdulkhaliq Arrhythmia heart disease classification using deep learning
description	Arrhythmia affects millions of people in the world. Sudden cardiac death is the cause about half of deaths due to cardiovascular disease and about 15% of all deaths globally. About 80% of sudden cardiac death is the result of ventricular arrhythmias. Arrhythmias may occur at any age but are more common among older people. Arrhythmias are caused by problems with the electrical conduction system of the heart. Therefore, we have designed a model using supervised deep learning to classify the heartbeats extracted from an ECG into four (4) heartbeat classes which is normal beat, ventricular ectopic beat (VEB), supraventricular ectopic beat (SVEB) and fusion beat, based only on the line shape (morphology) of the individual heartbeats. The overall performance of the system resulted in a precision of 95.378%, a recall of 81.3035%, accuracy of 97.62% and an F1 score 84.6875%.
format	Thesis
qualification_name	Master of Philosophy (M.Phil.)
qualification_level	Master's degree
author	Abdulkarim Farah, Abdulkhaliq
author_facet	Abdulkarim Farah, Abdulkhaliq
author_sort	Abdulkarim Farah, Abdulkhaliq
title	Arrhythmia heart disease classification using deep learning
title_short	Arrhythmia heart disease classification using deep learning
title_full	Arrhythmia heart disease classification using deep learning
title_fullStr	Arrhythmia heart disease classification using deep learning
title_full_unstemmed	Arrhythmia heart disease classification using deep learning
title_sort	arrhythmia heart disease classification using deep learning
granting_institution	Universiti Tun Hussein Onn Malaysia
granting_department	Fakulti Kejuruteraan Elektrik dan Elektronik
publishDate	2020
url	http://eprints.uthm.edu.my/375/1/24p%20ABDULKHALIQ%20ABDULKARIM%20FARAH.pdf http://eprints.uthm.edu.my/375/2/ABDULKHALIQ%20ABDULKARIM%20FARAH%20COPYRIGHT%20DECLARATION.pdf http://eprints.uthm.edu.my/375/3/ABDULKHALIQ%20ABDULKARIM%20FARAH%20WATERMARK.pdf
_version_	1747830595229057024

Arrhythmia heart disease classification using deep learning

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