Optimizing electrocardiogram analysis for efficient heart condition diagnosis

dc.contributor.advisorElgazzar, Khalid
dc.contributor.authorMousa, Ahmad
dc.date.accessioned2023-10-17T19:50:09Z
dc.date.available2023-10-17T19:50:09Z
dc.date.issued2023-09-01
dc.degree.disciplineElectrical and Computer Engineering
dc.degree.levelMaster of Applied Science (MASc)
dc.description.abstractThis 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.en
dc.description.sponsorshipUniversity of Ontario Institute of Technologyen
dc.identifier.urihttps://hdl.handle.net/10155/1695
dc.language.isoenen
dc.subjectECGen
dc.subjectStandard 12 lead ECG signalen
dc.subjectMulti-classen
dc.subjectClassificationen
dc.subjectLead groupen
dc.titleOptimizing electrocardiogram analysis for efficient heart condition diagnosisen
dc.typeThesisen
thesis.degree.disciplineElectrical and Computer Engineering
thesis.degree.grantorUniversity of Ontario Institute of Technology
thesis.degree.nameMaster of Applied Science (MASc)

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