{"id":{"repo_id":"uthm","oai_identifier":"oai:eprints.uthm.edu.my:375"},"canonical_url":"https://search.dev.ndltd.org/etd/uthm/oai:eprints.uthm.edu.my:375","repository":{"repo_id":"uthm","name":"Universiti Tun Hussein Onn Malaysia","base_url":"http://eprints.uthm.edu.my/cgi/oai2"},"display":{"title":"Arrhythmia heart disease classification using deep learning","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%.","abstract_html":"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%.","abstract_has_math":false,"creators":["Abdulkarim Farah, Abdulkhaliq"],"institution":"Universiti Tun Hussein Onn Malaysia","degree_name":"mphil","degree_level":"masters","degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2020,"date_issued":"2020-01","date_published":"2020-01","updated_at":"2026-07-24T05:47:49Z","subjects":["RC Internal medicine"],"languages":["en"],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":null,"outbound_label":null,"outbound_source":null},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:creator","label":"Author","values":["Abdulkarim Farah, Abdulkhaliq"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2020-01"]},{"key":"dc:date.issued","label":"Date","values":["2020-01"]},{"key":"dc:publisher.department","label":"Dc Publisher Department","values":["Fakulti Kejuruteraan Elektrik dan Elektronik"]},{"key":"dc:publisher.institution","label":"Dc Publisher Institution","values":["Universiti Tun Hussein Onn Malaysia"]},{"key":"dc:relation.isreferencedby","label":"Dc Relation Isreferencedby","values":["http://eprints.uthm.edu.my/375/"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"dc:type.qualificationlevel","label":"Dc Type Qualificationlevel","values":["masters"]},{"key":"dc:type.qualificationname","label":"Dc Type Qualificationname","values":["mphil"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["RC Internal medicine"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["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"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["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%."]},{"key":"dc:format","label":"Dc Format","values":["text"]},{"key":"dc:title","label":"Title","values":["Arrhythmia heart disease classification using deep learning"]}]}],"canonical_facts":{"dc:creator":["Abdulkarim Farah, Abdulkhaliq"],"dc:date":["2020-01"],"dc:date.issued":["2020-01"],"dc:description.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%."],"dc:format":["text"],"dc:identifier.uri":["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"],"dc:language":["en"],"dc:publisher.department":["Fakulti Kejuruteraan Elektrik dan Elektronik"],"dc:publisher.institution":["Universiti Tun Hussein Onn Malaysia"],"dc:relation.isreferencedby":["http://eprints.uthm.edu.my/375/"],"dc:subject":["RC Internal medicine"],"dc:title":["Arrhythmia heart disease classification using deep learning"],"dc:type":["Thesis"],"dc:type.qualificationlevel":["masters"],"dc:type.qualificationname":["mphil"]},"updated_at":"2026-07-24T05:47:49Z"}