Researchers aim to predict cardiac events with AI technique used to analyze earthquakes

This animation shows an ECG signal transitioning from a normal rhythm to an arrhythmia. In the top right corner is the model score for a particular pediatric arrhythmia called Junctional Ectopic Tachycardia (JET). When the signal transitions, you can see the model score increase. Sebastian Goodfellow , an assistant professor in the University of Toronto's department of civil and mineral engineering, and his team have partnered with researchers at The Hospital for Sick Children (SickKids) to help detect and diagnose heart arrhythmias. The project, supported by grant from the Canadian Institutes of Health Research, aims to leverage techniques developed by Goodfellow and his colleague in their previous work, which involves using artificial intelligence (AI) to analyze seismic data. While the goal in those projects was to learn how to recognize the signals that precede seismic events, such as earthquakes, the new project will focus on a different kind of data: that generated from electrocardiograms, or ECGs. Machine learning techniques developed in the context of geology could be adapted to pick up on the warning signals that precede cardiac events, such as arrhythmias, which affect roughly 700 critically ill children at SickKids each year. Goodfellow and his team are collaborating with Dr. Mjaye Mazwi - a staff physician in the department of critical care medicine at SickKids and an associate professor in University of Toronto's Temerty Faculty of Medicine - and the team at Laussen Labs, a multidisciplinary research group at SickKids. Writer Phill Snell sat down with Goodfellow to talk about the project. How did you get involved in this project?