Automated Method for Detecting Acute Insomnia Using Multi-Night Actigraphy Data

Angelova, Maia, Karmakar, Chandan, Zhu, Ye, Drummond, Sean P. A. and Ellis, Jason (2020) Automated Method for Detecting Acute Insomnia Using Multi-Night Actigraphy Data. IEEE Access, 8. pp. 74413-74422. ISSN 2169-3536

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In this paper we propose a new machine learning model for classification of nocturnal awakenings in acute insomnia and normal sleep. The model does not require sleep diaries or any other subjective information from the individuals who took part of the study. It is based on nocturnal actigraphy collected from pre-medicated individuals with acute insomnia and normal sleep controls. We have derived dynamical and statistical features from the actigraphy time series data. These features are combined using two machine learning techniques namely Random Forest (RF) and Support Vector Machine (SVM). RF shows better performance (accuracy-84%) than SVM (73%) in classifying individuals with insomnia from healthy sleepers. The developed model provides a signature of the condition of acute insomnia obtained from actigraphy only and is very promising as a tool to detect the condition in a non-invasive way and without sleep diaries or any other subjective information.

Item Type: Article
Additional Information: Funding Information: This work was supported by the Newton Advanced Fellowship through The Academy of Medical Sciences U.K. The work of Ye Zhu was supported by the Deakin University.
Uncontrolled Keywords: actigraphy, Acute insomnia, dynamical features, insomnia detection, machine learning
Subjects: B900 Others in Subjects allied to Medicine
C800 Psychology
C900 Others in Biological Sciences
Department: Faculties > Health and Life Sciences > Psychology
Depositing User: Rachel Branson
Date Deposited: 27 Apr 2021 08:18
Last Modified: 31 Jul 2021 16:01

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