@inproceedings{32cf6d9b8b73412cb51a69acb1aad69f,
title = "Patient-Specific Modeling of Daily Activity Patterns for Unsupervised Detection of Psychotic and Non-Psychotic Relapses",
abstract = "In this paper, we present our submission to the 2nd e-Prevention Grand Challenge hosted at ICASSP 2024. The objective posed in the challenge was to identify psychotic and non-psychotic relapses in patients using biosignals captured by wearable sensors. Our proposed solution is an unsupervised anomaly detection approach based on Transformers. We train individual models for each patient to predict the timestamps of biosignal measurements on non-relapse days, implicitly modeling normal daily routines. The models' mean-normalized prediction errors are then used as indicators of atypical behavior and, thus, risk of relapse. Our final submission ranked 3rd on detecting non-psychotic relapses (Track 1) and 1st on detecting psychotic relapses (Track 2).",
keywords = "Anomaly Detection, Machine Learning, Mental Health, Psychotic Disorders, Time-series",
author = "Alice Hein and Sven Gronauer and Klaus Diepold",
note = "Publisher Copyright: {\textcopyright} 2024 IEEE.; 49th IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops, ICASSPW 2024 ; Conference date: 14-04-2024 Through 19-04-2024",
year = "2024",
doi = "10.1109/ICASSPW62465.2024.10627246",
language = "English",
series = "2024 IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops, ICASSPW 2024 - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "43--44",
booktitle = "2024 IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops, ICASSPW 2024 - Proceedings",
}
