AUTHORS:
Sharon Ernst Chair of Clinical Psychology and Psychotherapy, Friedrich-Alexander-University, Erlangen-Nuremberg, Erlangen, Germany
Daniel Krauss Machine Learning and Data Analytics Lab., Friedrich-Alexander-University, Erlangen-Nuremberg, Erlangen, Germany
Moritz Moß Machine Learning and Data Analytics Lab., Friedrich-Alexander-University, Erlangen-Nuremberg, Erlangen, Germany
Martin Vossiek Chair of High Frequency Technology, Friedrich-Alexander-University, Erlangen-Nuremberg, Erlangen, Germany
Bjoern Eskofier Machine Learning and Data Analytics Lab., Friedrich-Alexander-University, Erlangen-Nuremberg, Erlangen, Germany
Matthias Berking Chair of Clinical Psychology and Psychotherapy, Friedrich-Alexander-University, Erlangen-Nuremberg, Erlangen, Germany

ABSTRACT:
Clinical research demonstrates the bidirectional associations between affect and sleep: Major depressive disorder is a prevalent clinical disorder, characterized by alterations in affect and sleep. Conversely, sleep disturbances emerge as prodromal manifestations preceding the onset of the disorder. Despite these close links, few studies investigated depressed daytime affect and its bidirectional associations with sad affect at the time of sleep. This lack of evidence can be attributed to methodological constraints, challenging the assessment of affect during sleep. To address this research gap, the present study introduces an innovative method utilizing Machine Learning and Infrared technology for the unobtrusive assessment of affect during sleep. The objectives of the present proof of concept were as follows: First, to introduce a novel approach for measuring affect during sleep. Second, to determine the bidirectional associations of depressed affect at daytime and sad affect during sleep in a healthy sample. Sad affect during sleep was measured using three infrared cameras with adaptive night vision, recording 63 videos during sleep. Daytime depressed affect was measured by survey, data analysis included general linear modeling (GLM). In line with the first objective, sad affect during sleep was extracted from video-based facial expressions using Artificial Intelligence software frameworks open DBM and open Face (AiCure). In line with the second objective, GLM analysis yielded that higher levels of depressed affect at daytime predicted higher levels of sad affect during sleep significantly. The inverse association was not significant. Conclusionally, we present an innovative method for the unobtrusive measurement of affect during sleep. This method may be particularly advantageous for the early detection of depressed mood states in subclinical populations and samples undergoing transient depressed mood states. Clinical and general implications are discussed.

Keywords: Depression, Sleep, Artificial Intelligence, Deep Neural Networks, Methodological Innovations

Conference Venue: Male, Maldives
Conference Date: 5-7 November 2024

ISBN Number: 978-625-00-7517-3
DOI Number: https://doi.org/10.53375/imhsc.2024.117


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