End-to-End Automatic Sleep Stage Classification Using Spectral-Temporal Sleep Features

Hyeong Jin Kim, Minji Lee, Seong Whan Lee

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

    7 Citations (Scopus)

    Abstract

    Sleep disorder is one of many neurological diseases that can affect greatly the quality of daily life. It is very burdensome to manually classify the sleep stages to detect sleep disorders. Therefore, the automatic sleep stage classification techniques are needed. However, the previous automatic sleep scoring methods using raw signals are still low classification performance. In this study, we proposed an end-to-end automatic sleep staging framework based on optimal spectral-temporal sleep features using a sleep-edf dataset. The input data were modified using a bandpass filter and then applied to a convolutional neural network model. For five sleep stage classification, the classification performance 85.6% and 91.1% using the raw input data and the proposed input, respectively. This result also shows the highest performance compared to conventional studies using the same dataset. The proposed framework has shown high performance by using optimal features associated with each sleep stage, which may help to find new features in the automatic sleep stage method.Clinical Relevance - The proposed framework would help to diagnose sleep disorders such as insomnia by improving sleep stage classification performance.

    Original languageEnglish
    Title of host publication42nd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society
    Subtitle of host publicationEnabling Innovative Technologies for Global Healthcare, EMBC 2020
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    Pages3452-3455
    Number of pages4
    ISBN (Electronic)9781728119908
    DOIs
    Publication statusPublished - 2020 Jul
    Event42nd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society, EMBC 2020 - Montreal, Canada
    Duration: 2020 Jul 202020 Jul 24

    Publication series

    NameProceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
    Volume2020-July
    ISSN (Print)1557-170X

    Conference

    Conference42nd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society, EMBC 2020
    Country/TerritoryCanada
    CityMontreal
    Period20/7/2020/7/24

    Bibliographical note

    Publisher Copyright:
    © 2020 IEEE.

    ASJC Scopus subject areas

    • Signal Processing
    • Biomedical Engineering
    • Computer Vision and Pattern Recognition
    • Health Informatics

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