Sudden unexpected death in epilepsy (SUDEP) is the leading cause of epilepsy-related death, especially in young people. Yet we still cannot reliably identify who is at greatest risk or how to prevent it.
This project brings together the largest international collection of SUDEP cases and matched living epilepsy controls with detailed biological data.
By analyzing patterns in heart rhythm, breathing, brain activity, arousal, and sleep, this project aims to uncover measurable warning signs of risk. Using advanced machine learning, we will combine these signals into a personalized risk prediction tool to guide targeted monitoring, inform prevention strategies, and ultimately save lives.
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