Manic episodes of bipolar disorder can lead to uncritical behaviour and delusional psychosis, often with destructive consequences for those affected and their surroundings. Early detection and intervention of a manic episode are crucial to prevent escalation, hospital admission and premature death. However, people with bipolar disorder may not recognize that they are experiencing a manic episode and symptoms such as euphoria and increased productivity can also deter affected individuals from seeking help. This work proposes to perform user-independent, automatic mood-state detection based on actigraphy and electrodermal activity acquired from a wrist-worn device during mania and after recovery (euthymia). This paper proposes a new deep learning-based ensemble method leveraging long (20h) and short (5 minutes) time-intervals to discriminate between the mood-states. When tested on 47 bipolar patients, the proposed classification scheme achieves an average accuracy of 91.59% in euthymic/manic mood-state recognition.
@article{arxiv.2107.00710,
title = {Long-Short Ensemble Network for Bipolar Manic-Euthymic State Recognition Based on Wrist-worn Sensors},
author = {Ulysse Côté-Allard and Petter Jakobsen and Andrea Stautland and Tine Nordgreen and Ole Bernt Fasmer and Ketil Joachim Oedegaard and Jim Torresen},
journal= {arXiv preprint arXiv:2107.00710},
year = {2022}
}
Comments
Published in IEEE Pervasive Computing in 2022. 12 pages + 2. 2 Figures and 3 tables