The Virtual Sleep Lab—A Novel Method for Accurate Four-Class Sleep Staging Using Heart-Rate Variability from Low-Cost Wearables

Pavlos Topalidis, Dominik Philip Johannes Heib, Sebastian Baron, Esther-Sevil Eigl, Alexandra Hinterberger, Manuel Schabus*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

Sleep staging based on polysomnography (PSG) performed by human experts is the de facto “gold standard” for the objective measurement of sleep. PSG and manual sleep staging is, however, personnel-intensive and time-consuming and it is thus impractical to monitor a person’s sleep architecture over extended periods. Here, we present a novel, low-cost, automatized, deep learning alternative to PSG sleep staging that provides a reliable epoch-by-epoch four-class sleep staging approach (Wake, Light [N1 + N2], Deep, REM) based solely on inter-beat-interval (IBI) data. Having trained a multi-resolution convolutional neural network (MCNN) on the IBIs of 8898 full-night manually sleep-staged recordings, we tested the MCNN on sleep classification using the IBIs of two low-cost (
Original languageEnglish
Article number2390
Number of pages17
JournalSensors
Volume23
Issue number5
DOIs
Publication statusPublished - 21 Feb 2023

Bibliographical note

Publisher Copyright:
© 2023 by the authors.

Keywords

  • automatic sleep staging
  • heart-rate variability
  • wearables
  • machine learning
  • digital CBT-I

Fields of Science and Technology Classification 2012

  • 501 Psychology

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