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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| Format: | Dissertation |
| Language: | English English English |
| Published: |
2020
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| Online Access: | http://eprints.uthm.edu.my/375/ |
| Abstract | Abstract here |
| _version_ | 1862665235866845184 |
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| author | Abdulkarim Farah, Abdulkhaliq |
| author_facet | Abdulkarim Farah, Abdulkhaliq |
| author_sort | Abdulkarim Farah, Abdulkhaliq |
| 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 | Dissertation |
| id | uthm-375 |
| institution | Universiti Tun Hussein Onn Malaysia |
| language | English English English |
| publishDate | 2020 |
| record_format | EPrints |
| record_pdf | Restricted |
| spelling | uthm-3752021-07-25T01:12:03Z http://eprints.uthm.edu.my/375/ Arrhythmia heart disease classification using deep learning 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 NonPeerReviewed text en http://eprints.uthm.edu.my/375/1/24p%20ABDULKHALIQ%20ABDULKARIM%20FARAH.pdf text en http://eprints.uthm.edu.my/375/2/ABDULKHALIQ%20ABDULKARIM%20FARAH%20COPYRIGHT%20DECLARATION.pdf text en http://eprints.uthm.edu.my/375/3/ABDULKHALIQ%20ABDULKARIM%20FARAH%20WATERMARK.pdf Abdulkarim Farah, Abdulkhaliq (2020) Arrhythmia heart disease classification using deep learning. Masters thesis, Universiti Tun Hussein Onn Malaysia. |
| spellingShingle | RC Internal medicine Abdulkarim Farah, Abdulkhaliq Arrhythmia heart disease classification using deep learning |
| thesis_level | Master |
| title | 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_short | Arrhythmia heart disease classification using deep learning |
| title_sort | arrhythmia heart disease classification using deep learning |
| topic | RC Internal medicine |
| url | http://eprints.uthm.edu.my/375/ |
| work_keys_str_mv | AT abdulkarimfarahabdulkhaliq arrhythmiaheartdiseaseclassificationusingdeeplearning |