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

Kusmakar, Shitanshu, Karmakar, Chandan, Zhu, Ye, Shelyag, Sergiy, Drummond, SPA, Ellis, JG and Angelova Turkedjieva, Maia 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, vol. 8, no. 6, pp. 1-17, doi: 10.1098/rsos.202264.

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Title A machine learning model for multi-night actigraphic detection of chronic insomnia: development and validation of a pre-screening tool
Author(s) Kusmakar, Shitanshu
Karmakar, ChandanORCID iD for Karmakar, Chandan orcid.org/0000-0003-1814-0856
Zhu, YeORCID iD for Zhu, Ye orcid.org/0000-0003-4776-4932
Shelyag, SergiyORCID iD for Shelyag, Sergiy orcid.org/0000-0002-6436-9347
Drummond, SPA
Ellis, JG
Angelova Turkedjieva, MaiaORCID iD for Angelova Turkedjieva, Maia orcid.org/0000-0002-0931-0916
Journal name Royal Society open science
Volume number 8
Issue number 6
Article ID 202264
Start page 1
End page 17
Total pages 17
Publisher Royal Society Publishing
Place of publication London, Eng.
Publication date 2021-06
ISSN 2054-5703
2054-5703
Keyword(s) actigraphy
chronic insomnia
dynamical features
machine learning
multi-night recordings
sleep
Summary 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.
Language eng
DOI 10.1098/rsos.202264
Indigenous content off
HERDC Research category C1 Refereed article in a scholarly journal
Grant ID APP1105458 (SPAD)
Free to Read? Yes
Persistent URL http://hdl.handle.net/10536/DRO/DU:30152479

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Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.