A machine learning model for multi-night actigraphic detection of chronic insomnia: development and validation of a pre-screening tool

Kusmakar, S., Karmakar, C., Zhu, Y., Shelyag, S., Drummond, S. P. A., Ellis, Jason and Angelova, M. (2021) A machine learning model for multi-night actigraphic detection of chronic insomnia: development and validation of a pre-screening tool. Royal Society Open Science, 8 (6). p. 202264. ISSN 2054-5703

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Official URL: https://doi.org/10.1098/rsos.202264

Abstract

We propose a novel machine learning-based method for analysing multi-night actigraphy signals to objectively classify and differentiate nocturnal awakenings in individuals with chronic insomnia (CI) and their cohabiting healthy partners. We analysed nocturnal actigraphy signals from 40 cohabiting couples with one partner seeking treatment for insomnia. We extracted 12 time-domain dynamic and nonlinear features from the actigraphy signals to classify nocturnal awakenings in healthy individuals and those with CI. These features were then used to train two machine learning classifiers, random forest (RF) and support vector machine (SVM). An optimization algorithm that incorporated the predicted quality of each night for each individual was used to classify individuals into CI or healthy sleepers. Using the proposed actigraphic signal analysis technique, coupled with a rigorous leave-one-out validation approach, we achieved a classification accuracy of 80% (sensitivity: 76%, specificity: 82%) in classifying CI individuals and their healthy bed partners. The RF classifier (accuracy: 80%) showed a better performance than SVM (accuracy: 75%). Our approach to analysing the multi-night nocturnal actigraphy recordings provides a new method for screening individuals with CI, using wrist-actigraphy devices, facilitating home monitoring.

Item Type: Article
Uncontrolled Keywords: actigraphy, sleep, chronic insomnia, multi-night recordings, dynamical features, machine learning
Subjects: B800 Medical Technology
C800 Psychology
G600 Software Engineering
Department: Faculties > Health and Life Sciences > Psychology
Depositing User: John Coen
Date Deposited: 17 Jun 2021 09:41
Last Modified: 17 Jun 2021 09:45
URI: http://nrl.northumbria.ac.uk/id/eprint/46469

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