At-home monitoring of abnormal sleep behaviors

Joint work with Dr. Anna Castelnovo from Ente Ospedaliero Cantonale (EOC)

This project explores the feasibility of an AI-powered device combining wearable EEG and video data. It aims to assess the feasibility of a user-friendly device for at-home sleep behavior monitoring. The developed device records the user’s synchronized video and EEG signals and includes algorithms for automated detection of relevant events for diagnosis, treatment monitoring, and patient safety. We develop machine learning models considering multimodal information from video-polysomnography recordings to detect abnormal events such as non-REM parasomnias.