Remote Arrhythmia Detection for Eldercare

Cardiovascular disease continues to be one of the most prevalent medical conditions in modern society, especially among elderly citizens. This thesis reviewed the challenges of automatically detecting heart arrhythmias and developed a platform for remote monitoring with an emphasis on early arrhythmia detection and scalability. Additionally, a novel two-phase classification scheme was proposed and applied to an existing electrocardiogram classification algorithm, improving its predictive performance for arrhythmia detection. Testing of a prototype setup was performed on 27 unique patients at the Sarawak General Hospital to evaluate the effectiveness and future applications of the work.