Comparison of feature and classifier algorithms for online automatic sleep staging based on a single EEG signal

Mustafa Radha, Gary Garcia Molina, Mannes Poel, Giulio Tononi

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    27 Citations (Scopus)
    123 Downloads (Pure)

    Abstract

    Automatic sleep staging on an online basis has recently emerged as a research topic motivated by fundamental sleep research. The aim of this paper is to find optimal signal processing methods and machine learning algorithms to achieve online sleep staging on the basis of a single EEG signal. The classification performance obtained using six different EEG signals and various signal processing feature sets is compared using the kappa statistic which has very recently become popular in sleep staging research. A variable duration of the EEG segment (or epoch) to decide on the sleep stage is also analyzed. Spectral-domain, time-domain, linear, and nonlinear features are compared in terms of performance and two types of machine learning approaches (random forests and support vector machines) are assessed. We have determined that frontal EEG signals, with spectral linear features, epoch durations between 18 and 30 seconds, and a random forest classifier lead to optimal classification performance while ensuring real-time online operation.
    Original languageUndefined
    Title of host publicationProceedings of the 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014
    Place of PublicationUSA
    PublisherIEEE Computer Society
    Pages1876-1880
    Number of pages5
    ISBN (Print)978-1-4244-7929-0
    DOIs
    Publication statusPublished - Aug 2014
    Event36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014 - Chicago, United States
    Duration: 26 Aug 201430 Aug 2014
    Conference number: 36

    Publication series

    Name
    PublisherIEEE Computer Society
    ISSN (Print)1557-170X

    Conference

    Conference36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014
    Abbreviated titleEMBC
    CountryUnited States
    CityChicago
    Period26/08/1430/08/14

    Keywords

    • EWI-25392
    • METIS-309706
    • IR-94098

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