Automatic sleep staging using heart rate variability, body movements, and recurrent neural networks in a sleep disordered population

Pedro Fonseca (Corresponding author), Merel M. van Gilst, Mustafa Radha, Marco Ross, Arnaud Moreau, Andreas Cerny, Peter Anderer, Xi Long, Johannes P. van Dijk, Sebastiaan Overeem

Research output: Contribution to journalArticleAcademicpeer-review

6 Citations (Scopus)

Abstract

STUDY OBJECTIVES: To validate a previously developed sleep staging algorithm using heart rate variability (HRV) and body movements in an independent broad cohort of unselected sleep disordered patients.

METHODS: We applied a previously designed algorithm for automatic sleep staging using long short-term memory recurrent neural networks to model sleep architecture. The classifier uses 132 HRV features computed from electrocardiography and activity counts from accelerometry. We retrained our algorithm using two public datasets containing both healthy sleepers and sleep disordered patients. We then tested the performance of the algorithm on an independent hold-out validation set of sleep recordings from a wide range of sleep disorders collected in a tertiary sleep medicine center.

RESULTS: The classifier achieved substantial agreement on four-class sleep staging (wake/N1-N2/N3/rapid eye movement [REM]), with an average κ of 0.60 and accuracy of 75.9%. The performance of the sleep staging algorithm was significantly higher in insomnia patients (κ = 0.62, accuracy = 77.3%). Only in REM parasomnias, the performance was significantly lower (κ = 0.47, accuracy = 70.5%). For two-class wake/sleep classification, the classifier achieved a κ of 0.65, with a sensitivity (to wake) of 72.9% and specificity of 94.0%.

CONCLUSIONS: This study shows that the combination of HRV, body movements, and a state-of-the-art deep neural network can reach substantial agreement in automatic sleep staging compared with polysomnography, even in patients suffering from a multitude of sleep disorders. The physiological signals required can be obtained in various ways, including non-obtrusive wrist-worn sensors, opening up new avenues for clinical diagnostics.

Original languageEnglish
Article numberzsaa048
Number of pages10
JournalSleep
Volume43
Issue number9
Early online date6 Apr 2020
DOIs
Publication statusPublished - 14 Sep 2020

Bibliographical note

© Sleep Research Society 2020. Published by Oxford University Press on behalf of the Sleep Research Society. All rights reserved. For permissions, please e-mail journals.permissions@oup.com.

Keywords

  • Actigraphy
  • Heart rate variability
  • Hypnogram
  • LSTM-model
  • Machine learning
  • Recurrent neural network
  • Sleep disorders
  • Sleep staging

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