Deep residual networks for automatic sleep stage classification of raw polysomnographic waveforms

Alexander Neergaard Olesen, Poul Jennum, Paul Peppard, Emmanuel Mignot, Helge Bjarup Dissing Sørensen

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    Abstract

    We have developed an automatic sleep stage classification algorithm based on deep residual neural networks and raw polysomnogram signals. Briefly, the raw data is passed through 50 convolutional layers before subsequent classification into one of five sleep stages. Three model configurations were trained on 1850 polysomnogram recordings and subsequently tested on 230 independent recordings. Our best performing model yielded an accuracy of 84.1% and a Cohen’s kappa of 0.746, improving on previous reported results by other groups also using only raw polysomnogram data. Most errors were made on non-REM stage 1 and 3 decisions, errors likely resulting from the definition of these stages. Further testing on independent cohorts is needed to verify performance for clinical use.
    Original languageEnglish
    Title of host publicationProceedings of 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society
    PublisherIEEE
    Publication date2018
    Pages3713-3716
    ISBN (Print)9781538636459
    DOIs
    Publication statusPublished - 2018
    Event40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society - Hawaii Convention Center, Honolulu, United States
    Duration: 17 Jul 201821 Jul 2018
    Conference number: 40
    https://embc.embs.org/2018/

    Conference

    Conference40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society
    Number40
    LocationHawaii Convention Center
    Country/TerritoryUnited States
    CityHonolulu
    Period17/07/201821/07/2018
    Internet address

    Keywords

    • Sleep
    • Brain modeling
    • Electroencephalography
    • Tensile stress
    • Electrooculography

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