Relapse Detection in Patients with Psychotic Disorders Using Unsupervised Learning on Smartwatch Signals
Salam Hamieh, Vincent Heiries, Hussein Al Osman, Christelle Godin
Abstract
Relapse detection is a crucial component of mental disorders treatment and management. In this paper, we present our solution for the ICASSP Signal Processing Grand Challenge e-Prevention track 2 Relapse Detection. We propose an unsupervised learning approach to detect relapse in patients with mental health disorders using anomaly detection with an autoencoder. To this end, we extract features from sleep, physical activity, and physiological data recordings. We train an autoencoder exclusively on non-relapse data. To detect relapses, we calculate the reconstruction error of the autoencoder and use it as an anomaly score. Our team, Emotion, ranked second in the e-prevention challenge with a ROC-AUC score of 0.6 and a PR-AUC score of 0.63 on the testing dataset.
BibTeX
@inproceedings{icassp2023_relapsedetection,
title = {Relapse Detection in Patients with Psychotic Disorders Using Unsupervised Learning on Smartwatch Signals},
author = {Salam Hamieh and Vincent Heiries and Hussein Al Osman and Christelle Godin},
booktitle = {ICASSP 2023},
year = {2023}
}