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Weighted Performance Metrics for Automatic Neonatal Seizure Detection Using Multiscored EEG Data.

Amir Hossein Ansari, Perumpillichira Joseph Cherian, Alexander Caicedo Dorado

    IEEE Journal of Biomedical and Health Informatics
    |September 15, 2017
    PubMed
    Summary

    Developing new metrics for automated seizure detection in neonatal electroencephalogram (EEG) is crucial. These novel metrics offer improved accuracy and robustness compared to traditional methods for evaluating seizure detection performance.

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    Area of Science:

    • Clinical Neurophysiology
    • Biomedical Engineering
    • Neonatal Medicine

    Background:

    • Continuous electroencephalogram (EEG) monitoring is essential in neonatal intensive care units (NICUs) for seizure detection.
    • Accurate labeling of neonatal seizures on EEG is challenging due to ambiguous seizure discharge characteristics.
    • Existing performance metrics for automated seizure detectors have limitations when dealing with inter-rater variability.

    Purpose of the Study:

    • To develop and validate novel performance metrics for automated neonatal seizure detection systems.
    • To address the limitations of traditional metrics (e.g., Good Detection Rate, False Alarm Rate) in handling expert disagreement.
    • To propose metrics that account for varying expert scores rather than relying on consensus or majority voting.

    Main Methods:

    • Utilized 353 hours of neonatal EEG data from 81 neonates, including visually scored seizures.
    • An automated seizure detector processed the EEG data, with its outputs combined with expert-scored seizures.
    • Three independent EEG readers relabeled the combined data, and these multiple labels were incorporated into modified performance metrics.

    Main Results:

    • The proposed performance metrics demonstrated higher accuracy and robustness compared to the majority voting technique.
    • Modified metrics effectively incorporated multiple expert scores, providing a more nuanced evaluation of detector performance.
    • Results were validated using a bootstrapping test, confirming the reliability of the new metrics.

    Conclusions:

    • The developed performance metrics offer a more accurate and robust evaluation of automated neonatal seizure detection.
    • These novel metrics are better suited to handle the inherent subjectivity and disagreement in expert EEG seizure scoring.
    • Implementation of these metrics can lead to more reliable development and validation of clinical seizure detection tools.