Optimizing electrocardiogram analysis for efficient heart condition diagnosis

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.

Name thesis:degree_name

Master of Applied Science (MASc)

Discipline thesis:degree_discipline

Electrical and Computer Engineering

Grantor

University of Ontario Institute of Technology

Year dc:date.issued

2023

Author dc:creator

• Mousa, Ahmad

Advisor dc:contributor.advisor

• Elgazzar, Khalid

• ECG
• Standard 12 lead ECG signal
• Multi-class
• Classification
• Lead group

Language dc:language.iso

en

Handle dc:identifier.uri

https://hdl.handle.net/10155/1695

OAI identifier oai:identifier

oai:ontariotechu.scholaris.ca:10155/1695