L2M – Digital Twin for Real-Time Cardiac Arrest Risk Monitoring

This project has developed a real-time digital monitoring system that identifies people at risk of sudden cardiac arrest by analyzing their heart signals (ECGs). By combining a machine learning model with cloud-based technology, the system automatically updates each patient’s status and sends alerts if a risk is detected. This enables the partner organization to provide faster, more accurate patient monitoring in hospitals or remotely, allowing earlier interventions that could save lives. The system makes heart monitoring more proactive and personalized, improving patient care and safety.

Faculty Supervisor:

Behrouz Far;Emad Mohammed

Student:

Partner:

Edmonton Unlimited

Discipline:

Engineering

Sector:

Professional, scientific and technical services; Public administration

University:

University of Calgary

Program:

Business Strategy Internship

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