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    This study introduces an automated system using recurrent neural networks (RNN) to accurately detect cyclic alternating patterns (CAP) and their subtypes in sleep EEG. The novel approach significantly improves classification performance for neurological condition monitoring.

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    Area of Science:

    • Neuroscience
    • Computational Biology
    • Signal Processing

    Background:

    • Cyclic alternating patterns (CAP) are transient brain activity perturbations during sleep, linked to neurological disorders.
    • CAP sequences consist of consecutive phasic activation (A-phases), requiring precise identification.

    Purpose of the Study:

    • To develop a novel, automated system for classifying A-phases and their subtypes using electroencephalography (EEG) data.
    • To leverage temporal dynamics in EEG signals for improved CAP detection and classification.

    Main Methods:

    • Utilized recurrent neural networks (RNN) to extract temporal information from EEG recordings.
    • Implemented signal processing methods to mitigate inter-subject variation and address imbalanced datasets.
    • Evaluated the system on the publicly available CAP Sleep Database.

    Main Results:

    • The RNN-based system demonstrated a 3-5% improvement in detection accuracy and a 7% increase in F1-score compared to feed-forward networks.
    • Achieved high sensitivity (76-78%) and F1-scores (63-68%) for CAP detection, outperforming existing methods.
    • Showcased superior multi-class classification for subtypes: A1 (60-63%), A2 (42-45%), and A3 (71-74%).

    Conclusions:

    • A fully automated, high-performance CAP scoring system with A-phase subtype classification has been developed.
    • RNN classifiers offer significant improvements in accuracy and sensitivity for CAP detection and classification.
    • The system provides a valuable tool for analyzing sleep disturbances and their association with neurological pathologies.