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Predicting brain age based on sleep EEG and DenseNet.

Soonhyun Yook, Yizhan Miao, Claire Park

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |December 11, 2021
    PubMed
    Summary

    We developed a novel sleep EEG model to predict brain age, achieving high accuracy. This brain age index correlates with sleep disorder severity, offering a new diagnostic tool for brain health.

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    Area of Science:

    • Neuroscience
    • Sleep Medicine
    • Artificial Intelligence

    Background:

    • Brain age prediction is a growing field for assessing neurological health.
    • Existing models often lack accuracy or comprehensive analysis of sleep-related factors.
    • Sleep disorders are increasingly linked to accelerated brain aging.

    Purpose of the Study:

    • To develop and validate a highly accurate sleep electroencephalography (EEG)-based model for brain age prediction.
    • To investigate the association between predicted brain age acceleration and common sleep disorders.
    • To establish the utility of a brain age index as a biomarker for sleep disorder-related brain health changes.

    Main Methods:

    • Acquisition of six-channel sleep EEG data over 6 hours.
    • Conversion of EEG data into 2D scalograms for analysis.
    • Utilizing a DenseNet architecture for brain age prediction.
    • Evaluating the correlation between chronological age and predicted brain age.
    • Assessing the relationship between brain aging acceleration and sleep disorders like insomnia and obstructive sleep apnea (OSA).

    Main Results:

    • The proposed model achieved an 80% correlation between chronological age and predicted brain age.
    • The mean absolute error for brain age prediction was 5.4 years.
    • Brain age was found to increase with the severity of sleep disorders.
    • Demonstrated a significant association between brain aging acceleration and sleep disorders.

    Conclusions:

    • The developed sleep EEG-based brain age prediction model demonstrates high accuracy and clinical relevance.
    • The brain age index serves as a sensitive biomarker for detecting brain health changes associated with sleep disorders.
    • This index can potentially be used as a diagnostic tool for individuals with various sleep disorders.