Automatic Detection of Cortical Arousals in Sleep using Bi-direction LSTM Networks

A. Brink-Kjaer, Alexander Neergaard Olesen, C. A. Jespersen, P. E. Peppard, P. J. Jennum, H. B. Sorensen, E. Mignot

    Research output: Contribution to journalConference abstract in journalResearchpeer-review

    440 Downloads (Pure)

    Abstract

    Cortical arousals are transient events that occur during sleep. Although they can occur naturally, arousals are often used to evaluate sleep-wake dysfunction. The gold standard for detecting arousals is visual inspection of polysomnography recordings. Manual annotation of arousals is time consuming and has been shown to have a high inter- and intra-scorer variation. This study proposes a method to fully automate detection of arousals using recent advances in machine learning.
    Methods:
    The proposed method in this study extracted features from electroencephalography (EEG), electrooculography (EOG) and chin electromyography (EMG) to compute a probability of arousals through a bi-directional long short-term memory neural network. The study used a dataset of 233 nocturnal PSGs of population-based samples from Wisconsin Sleep Cohort (WSC) and 30 nocturnal PSGs of clinical samples from the Stanford Sleep Cohort (SSC). The model was trained on 186 recordings from WSC and annotations from two scorers. The model was tested on 47 recordings from WSC and then compared to a set of 3 annotations from 9 independent scorers on 30 recordings from both cohorts by measure of Fleiss’ Kappa (level of agreement greater than chance).
    Results:
    The model obtained a precision of 0.79, a recall of 0.8 and F1-score of 0.79 on the 47 recordings from WSC. The model was robust to different sleep stages showing an F1-score of 0.71 ± 0.19, 0.8 ± 0.13, 0.89 ± 0.18 and 0.8 ± 0.17 (mean ± SD) for N1, N2, N3 and REM sleep, respectively. Preliminary results comparing the scorers show a Fleiss’ Kappa of 0.38 ± 0.12, while including the model predictions result in a Fleiss’ Kappa of 0.4 ± 0.1.
    Conclusion:
    Cortical arousals were detected automatically with the proposed algorithm with a high performance and robustness to different sleep stages. Preliminary results comparing nine independent scorers demonstrates a low inter-scorer reliability with a similar agreement to the model predictions.
    Original languageEnglish
    JournalSleep
    Volume41
    Issue numberAbstract Supplement
    Pages (from-to)A55-A56
    ISSN0161-8105
    DOIs
    Publication statusPublished - 2018
    Event32nd Annual Meeting of the Associated Professional Sleep Societies - Baltimore Convention Center, Baltimore, United States
    Duration: 2 Jun 20186 Jun 2018
    Conference number: 32

    Conference

    Conference32nd Annual Meeting of the Associated Professional Sleep Societies
    Number32
    LocationBaltimore Convention Center
    Country/TerritoryUnited States
    CityBaltimore
    Period02/06/201806/06/2018

    Fingerprint

    Dive into the research topics of 'Automatic Detection of Cortical Arousals in Sleep using Bi-direction LSTM Networks'. Together they form a unique fingerprint.

    Cite this