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

Maia Angelova*, Chandan Karmakar, Ye Zhu, Sean P.A. Drummond, Jason Ellis

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish
Article number9072096
Pages (from-to)74413-74422
Number of pages10
JournalIEEE Access
Publication statusPublished - 20 Apr 2020

Bibliographical note

Publisher Copyright:
© 2013 IEEE.


  • actigraphy
  • Acute insomnia
  • dynamical features
  • insomnia detection
  • machine learning


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