A Self-Supervised Learning Approach for Detecting Non-Psychotic Relapses Using Wearable-Based Digital Phenotyping

Panagiotis Kaliosis, Sofia Eleftheriou, Christos Nikou, Theodoros Giannakopoulos

Abstract: We present MagCIL’s approach for the 1st track of the "2nd e-Prevention challenge: Psychotic and Non-Psychotic Relapse Detection using Wearable-Based Digital Phenotyping". First we present our approach for preprocessing and extracting features from the wearable’s raw data. We then propose a Transformer model for learning self-supervised representations from augmented features, trained on data from non-relapse days from each of the 9 patients of the challenge. We adopt two unsupervised methods for detecting relapse days as outliers. A separate unsupervised model is tuned for each patient using the validation data of the challenge. Our method ranked second with ROCAUC = 0.651 and PRAUC = 0.642 on the final test dataset of the challenge. The respective code is available at this repository.