  This paper presents the approach and results of USC SAIL's submission to the
Signal Processing Grand Challenge 2023 - e-Prevention (Task 2), on detecting
relapses in psychotic patients. Relapse prediction has proven to be
challenging, primarily due to the heterogeneity of symptoms and responses to
treatment between individuals. We address these challenges by investigating the
use of sleep behavior features to estimate relapse days as outliers in an
unsupervised machine learning setting. We extract informative features from
human activity and heart rate data collected in the wild, and evaluate various
combinations of feature types and time resolutions. We found that short-time
sleep behavior features outperformed their awake counterparts and larger time
intervals. Our submission was ranked 3rd in the Task's official leaderboard,
demonstrating the potential of such features as an objective and non-invasive
predictor of psychotic relapses.
