Automatic sleep staging using a small-footprint sensor array and recurrent-convolutional neural networks

William G. Coon, Naresh M. Punjabi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The accelerating trend towards personalized 'pre-cision medicine' and tele-healthcare is revolutionizing the practice of medicine and giving the individual unprecedented access to their own health data. At the same time, a widening gap between wakeful health (ex. physical activity) and nocturnal health (sleep) has revealed the need for accurate, reliable and automated methods to measure sleep in the home. Here we describe a small-footprint sensor array, using electrode stickers that can be self-applied to the forehead, in conjunction with an automated scoring algorithm that achieves accuracies on par with trained human experts (77% agreement using a five-class taxonomy). Compared to alternatives, this approach avoids the low signal-to-noise ratios of dry-contact scalp electrodes while also circumventing the need to measure through hair. Critically, it does not require a trained human expert, either to apply the electrodes or to translate the signals into a useful description of sleep patterns. Taken together, this represents an exciting step forward towards affordable, reliable, and accurate in-the-home sleep assessment.

Original languageEnglish (US)
Title of host publication2021 10th International IEEE/EMBS Conference on Neural Engineering, NER 2021
PublisherIEEE Computer Society
Pages1144-1147
Number of pages4
ISBN (Electronic)9781728143378
DOIs
StatePublished - May 4 2021
Event10th International IEEE/EMBS Conference on Neural Engineering, NER 2021 - Virtual, Online, Italy
Duration: May 4 2021May 6 2021

Publication series

NameInternational IEEE/EMBS Conference on Neural Engineering, NER
Volume2021-May
ISSN (Print)1948-3546
ISSN (Electronic)1948-3554

Conference

Conference10th International IEEE/EMBS Conference on Neural Engineering, NER 2021
Country/TerritoryItaly
CityVirtual, Online
Period5/4/215/6/21

ASJC Scopus subject areas

  • Artificial Intelligence
  • Mechanical Engineering

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