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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, vol. 8, pp. 74413-74422, doi: 10.1109/access.2020.2988722.

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Title Automated Method for Detecting Acute Insomnia Using Multi-Night Actigraphy Data
Author(s) Angelova, MaiaORCID iD for Angelova, Maia orcid.org/0000-0002-0931-0916
Karmakar, ChandanORCID iD for Karmakar, Chandan orcid.org/0000-0003-1814-0856
Zhu, YeORCID iD for Zhu, Ye orcid.org/0000-0003-4776-4932
Drummond, Sean P. A.
Ellis, Jason
Journal name IEEE Access
Volume number 8
Start page 74413
End page 74422
Total pages 10
Publisher Institute of Electrical and Electronics Engineers (IEEE)
Place of publication Piscataway, N.J.
Publication date 2020-04-20
ISSN 2169-3536
2169-3536
Keyword(s) acute insomnia
actigraphy
machine learning
insomnia detection
dynamical features
Summary 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.
Language eng
DOI 10.1109/access.2020.2988722
Indigenous content off
HERDC Research category C1 Refereed article in a scholarly journal
Free to Read? Yes
Use Rights Creative Commons Attribution licence
Persistent URL http://hdl.handle.net/10536/DRO/DU:30136572

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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.