{"id":{"repo_id":"uoit","oai_identifier":"oai:ontariotechu.scholaris.ca:10155/1695"},"canonical_url":"https://search.dev.ndltd.org/etd/uoit/oai:ontariotechu.scholaris.ca:10155/1695","repository":{"repo_id":"uoit","name":"Ontario Institute of Technology","base_url":"https://ontariotechu.scholaris.ca/server/oai/request"},"display":{"title":"Optimizing electrocardiogram analysis for efficient heart condition diagnosis","abstract":"This thesis introduces an innovative lead grouping strategy for efficient real-time Electrocardiography signal classification. This method uses a maximum of six leads instead of the traditional 12-lead approach, leading to significant reductions in sampling time (93.67%), data size at the data acquisition device (50%), and signal processing time (84.72%). Importantly, these benefits come with a minimal loss in accuracy (0.08%). The thesis presents the CardioDiverse dataset, a publicly available resource that highlights key ECG leads associated with specific cardiovascular conditions. This resource can transform ECG-based diagnoses by focusing on the most pertinent leads. The proposed lead grouping strategy has been successfully integrated with a real-time platform, demonstrating its practical robustness and applicability. This contribution brings a considerable change in the field of ECG analysis by providing an efficient and viable lead grouping method that balances accuracy and resource efficiency, marking significant advances in ECG analysis.","abstract_html":"This thesis introduces an innovative lead grouping strategy for efficient real-time Electrocardiography signal classification. This method uses a maximum of six leads instead of the traditional 12-lead approach, leading to significant reductions in sampling time (93.67%), data size at the data acquisition device (50%), and signal processing time (84.72%). Importantly, these benefits come with a minimal loss in accuracy (0.08%). The thesis presents the CardioDiverse dataset, a publicly available resource that highlights key ECG leads associated with specific cardiovascular conditions. This resource can transform ECG-based diagnoses by focusing on the most pertinent leads. The proposed lead grouping strategy has been successfully integrated with a real-time platform, demonstrating its practical robustness and applicability. This contribution brings a considerable change in the field of ECG analysis by providing an efficient and viable lead grouping method that balances accuracy and resource efficiency, marking significant advances in ECG analysis.","abstract_has_math":false,"creators":["Mousa, Ahmad"],"institution":"University of Ontario Institute of Technology","degree_name":"Master of Applied Science (MASc)","degree_level":null,"degree_discipline":"Electrical and Computer Engineering","degree_department":null,"school":null,"contributors":[],"advisors":["Elgazzar, Khalid"],"committee_chairs":[],"committee_members":[],"year":2023,"date_issued":"2023-09-01","date_published":"2023-09-01","updated_at":"2026-07-24T05:35:43Z","subjects":["ECG","Standard 12 lead ECG signal","Multi-class","Classification","Lead group"],"languages":["en"],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/10155/1695","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Elgazzar, Khalid"]},{"key":"dc:creator","label":"Author","values":["Mousa, Ahmad"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2023-10-17T19:50:09Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2023-10-17T19:50:09Z"]},{"key":"dc:date.issued","label":"Date","values":["2023-09-01"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Electrical and Computer Engineering"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Applied Science (MASc)"]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Ontario Institute of Technology"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["ECG","Standard 12 lead ECG signal","Multi-class","Classification","Lead group"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["en"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://hdl.handle.net/10155/1695"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["This thesis introduces an innovative lead grouping strategy for efficient real-time Electrocardiography signal classification. 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Importantly, these benefits come with a minimal loss in accuracy (0.08%). The thesis presents the CardioDiverse dataset, a publicly available resource that highlights key ECG leads associated with specific cardiovascular conditions. This resource can transform ECG-based diagnoses by focusing on the most pertinent leads. The proposed lead grouping strategy has been successfully integrated with a real-time platform, demonstrating its practical robustness and applicability. 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