Grant: Canada Postdoctoral Research Award (CPRA) program

Sudden cardiac arrest (SCA) affects ~60,000 Canadians annually. Survival is very low when arrests are unwitnessed because nobody calls 9-1-1. My postdoctoral program will advance and evaluate a smartwatch-based system that detects SCA and initiates rapid Emergency Medical System (EMS) response. Building on published algorithms and a unique clinical dataset collected from end of life settings, I will (1) finalize a real-time detection and alerting pipeline on Samsung Galaxy Watch, (2) prospectively validate sensitivity, specificity, and reliability in clinical settings, and (3) integrate with provincial 9-1-1 systems to enable automated alerts with location and status data. I will also design a pragmatic community pilot to estimate real-world performance. The innovation is twofold: first, using clinical arrest data (not just simulated occlusions) to train and tune models; second, creating an end-to-end pathway from wrist to ambulance dispatch compatible with Next-Generation 9-1-1. Training-wise, I will gain advanced skills in machine learning for physiological time-series, regulatory pathways, and EMS informatics, supported by a multi-disciplinary mentorship team in emergency medicine, biomedical engineering, and data science. If successful, this work will enable earlier recognition of cardiac arrest, shorten time to intervention, and ultimately improve survival for unwitnessed events. The knowledge gained will generalize to other time-critical emergencies detectable by wearables.