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    This study introduces an automated sleep staging model using EEG and EOG data to aid in diagnosing REM sleep behavior disorder. The model achieved 92.6% accuracy, offering a promising step towards automated diagnosis.

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    Area of Science:

    • Neurology
    • Sleep Medicine
    • Biomedical Engineering

    Background:

    • Rapid eye movement (REM) sleep behavior disorder (RBD) is a prodromal stage of alpha-synucleinopathies.
    • Manual sleep staging for diagnosis is inconsistent and costly.
    • Automated methods are needed for accurate and efficient diagnosis.

    Purpose of the Study:

    • To develop and validate an automated sleep staging model using electroencephalography (EEG) and electrooculography (EOG) data.
    • To improve the diagnostic process for REM sleep behavior disorder (RBD).

    Main Methods:

    • Utilized the ISRUC-Sleep database for model optimization and training.
    • Engineered a random forest classifier using time and frequency-domain features from 33-second epochs.
    • Implemented a 20-fold cross-validation scheme for performance testing.

    Main Results:

    • The automated model achieved an overall accuracy of 92.6% and a Cohen's kappa of 0.856 on agreed-upon epochs.
    • The model successfully classified sleep stages (wakefulness, REM, non-REM) with high precision.
    • Demonstrated robust performance in differentiating sleep stages from EEG and EOG signals.

    Conclusions:

    • The developed automated sleep staging model shows significant promise for the diagnosis of RBD.
    • Further validation in patients with RBD is required.
    • This automated approach offers a potential solution to the limitations of manual sleep staging.