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This paper presents the approach and results of USC SAIL's submission to theSignal Processing Grand Challenge 2023 - e-Prevention (Task 2), on detectingrelapses in psychotic patients. Relapse prediction has proven to bechallenging, primarily due to the heterogeneity of symptoms and responses totreatment between individuals. We address these challenges by investigating theuse of sleep behavior features to estimate relapse days as outliers in anunsupervised machine learning setting. We extract informative features fromhuman activity and heart rate data collected in the wild, and evaluate variouscombinations of feature types and time resolutions. We found that short-timesleep behavior features outperformed their awake counterparts and larger timeintervals. Our submission was ranked 3rd in the Task's official leaderboard,demonstrating the potential of such features as an objective and non-invasivepredictor of psychotic relapses.

Signal Processing Grand Challenge 2023 -- e-Prevention: Sleep Behavior as an Indicator of Relapses in Psychotic Patients