Abdulkarim Farah, Abdulkhaliq (2020) Arrhythmia heart disease classification using deep learning. Masters thesis, Universiti Tun Hussein Onn Malaysia.
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Abstract
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%.
| Item Type: | Thesis (Masters) |
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| Subjects: | R Medicine > RC Internal medicine |
| Divisions: | Faculty of Electrical and Electronic Engineering > Department of Electrical Engineering |
| Depositing User: | Mrs. Sabarina Che Mat |
| Date Deposited: | 25 Jul 2021 01:12 |
| Last Modified: | 25 Jul 2021 01:12 |
| URI: | http://eprints.uthm.edu.my/id/eprint/375 |
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