Skip to main content

Main menu

  • Home
  • Current issue
  • Early View
  • Archive
  • Authors/reviewers
    • Instructions for authors
    • Submit a manuscript
    • COVID-19 submission information
    • Institutional open access agreements
    • Peer reviewer login
  • Alerts
  • Subscriptions
  • ERS Publications
    • European Respiratory Journal
    • ERJ Open Research
    • European Respiratory Review
    • Breathe
    • ERS Books
    • ERS publications home

User menu

  • Log in
  • Subscribe
  • Contact Us
  • My Cart

Search

  • Advanced search
  • ERS Publications
    • European Respiratory Journal
    • ERJ Open Research
    • European Respiratory Review
    • Breathe
    • ERS Books
    • ERS publications home

Login

European Respiratory Society

Advanced Search

  • Home
  • Current issue
  • Early View
  • Archive
  • Authors/reviewers
    • Instructions for authors
    • Submit a manuscript
    • COVID-19 submission information
    • Institutional open access agreements
    • Peer reviewer login
  • Alerts
  • Subscriptions

Instantaneous Heart Rate based sleep staging using deep learning models as a convenient alternative to Polysomnography

Y Jie Chen, Z Siting, K Kishan, A Patanaik
ERJ Open Research 2021 7: 63; DOI: 10.1183/23120541.sleepandbreathing-2021.63
Y Jie Chen
Neurobit Technologies, Singapore, Singapore
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Z Siting
Neurobit Technologies, Singapore, Singapore
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
K Kishan
Neurobit Technologies, Singapore, Singapore
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
A Patanaik
Neurobit Technologies, Singapore, Singapore
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • Article
  • Figures & Data
  • Info & Metrics
Loading

Abstract

Introduction/Objective: To date, polysomnography (PSG) remains the 'gold-standard' diagnosis for sleep disorders. Although, the high cost and intrusive nature of the test limits its reach. Prior research indicates that Instantaneous Heart Rate (IHR) might be an accurate and accessible physiological proxy for sleep measurement (Sridhar et al., 2020, npj Digital Medicine). We seek to validate the sleep staging performance of Deep Learning models (Z3Score®-HRV, Neurobit Technologies Pte Ltd) with the CINC open dataset (N=994 Subjects, Goldberger et al., 2020) using IHR derived from a single-channel ECG.

Methods: Single channel ECG was processed on the cloud using the Z3Score® System (https://www.z3score.com/) and was scored in 30-second epochs.

Results: We achieved an accuracy of 72.8% and a Cohen’s kappa of 0.54 on a 4-class staging (Figure 1A). On sleep vs wake classification, we achieved an overall accuracy of 87.7% with sensitivity of 63.3% and specificity of 93.0% (Figure 1B).

Conclusion: Our results demonstrated the utility of IHR derived from ECG for accurate sleep measurement. Sleep-wake detection was also significantly better than any pure actigraphy-based technologies at present. Our results demonstrate that popular ECG-patches (e.g. Movesense, Suunto, Finland) can be a more convenient and affordable alternative to PSG for sleep measurements.

Figure
  • Download figure
  • Open in new tab
  • Download powerpoint

  • Diagnostic
  • Telemonitoring
  • Telemonitoring

Footnotes

Cite this article as ERJ Open Research 2021; 7: Suppl. 7, 63.

This is an ERS Lung Science Conference abstract. No full-text version is available. Further material to accompany this abstract may be available at www.ers-education.org (ERS member access only).

  • Copyright ©the authors 2021
Next
Back to top
Vol 7 Issue suppl 7 Table of Contents
  • Table of Contents
  • Index by author
Email

Thank you for your interest in spreading the word on European Respiratory Society .

NOTE: We only request your email address so that the person you are recommending the page to knows that you wanted them to see it, and that it is not junk mail. We do not capture any email address.

Enter multiple addresses on separate lines or separate them with commas.
Instantaneous Heart Rate based sleep staging using deep learning models as a convenient alternative to Polysomnography
(Your Name) has sent you a message from European Respiratory Society
(Your Name) thought you would like to see the European Respiratory Society web site.
CAPTCHA
This question is for testing whether or not you are a human visitor and to prevent automated spam submissions.
Citation Tools
Instantaneous Heart Rate based sleep staging using deep learning models as a convenient alternative to Polysomnography
Y Jie Chen, Z Siting, K Kishan, A Patanaik
ERJ Open Research Apr 2021, 7 (suppl 7) 63; DOI: 10.1183/23120541.sleepandbreathing-2021.63

Citation Manager Formats

  • BibTeX
  • Bookends
  • EasyBib
  • EndNote (tagged)
  • EndNote 8 (xml)
  • Medlars
  • Mendeley
  • Papers
  • RefWorks Tagged
  • Ref Manager
  • RIS
  • Zotero
Share
Instantaneous Heart Rate based sleep staging using deep learning models as a convenient alternative to Polysomnography
Y Jie Chen, Z Siting, K Kishan, A Patanaik
ERJ Open Research Apr 2021, 7 (suppl 7) 63; DOI: 10.1183/23120541.sleepandbreathing-2021.63
Reddit logo Technorati logo Twitter logo Connotea logo Facebook logo Mendeley logo

Jump To

  • Article
  • Figures & Data
  • Info & Metrics
  • Tweet Widget
  • Facebook Like
  • Google Plus One

More in this TOC Section

  • Association between sleep-disordered breathing and MRI-based brain iron content
  • Pitolisant long term effect in sleepy obstructive sleep apnea patients with CPAP
  • Epworth Sleepiness Scale and NoSAS score as screening tools for Obstructive Sleep Apnea (OSA)
Show more Sleep & Breathing disorders

Related Articles

Navigate

  • Home
  • Current issue
  • Archive

About ERJ Open Research

  • Editorial board
  • Journal information
  • Press
  • Permissions and reprints
  • Advertising

The European Respiratory Society

  • Society home
  • myERS
  • Privacy policy
  • Accessibility

ERS publications

  • European Respiratory Journal
  • ERJ Open Research
  • European Respiratory Review
  • Breathe
  • ERS books online
  • ERS Bookshop

Help

  • Feedback

For authors

  • Instructions for authors
  • Publication ethics and malpractice
  • Submit a manuscript

For readers

  • Alerts
  • Subjects
  • RSS

Subscriptions

  • Accessing the ERS publications

Contact us

European Respiratory Society
442 Glossop Road
Sheffield S10 2PX
United Kingdom
Tel: +44 114 2672860
Email: journals@ersnet.org

ISSN

Online ISSN: 2312-0541

Copyright © 2023 by the European Respiratory Society