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Updated: May 24, 2025

Preterm EEG: A Multimodal Neurophysiological Protocol
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Multidomain Selective Feature Fusion and Stacking Based Ensemble Framework for EEG-Based Neonatal Sleep

Muhammad Irfan, Laishuan Wang, Husnain Shahid

    IEEE Journal of Biomedical and Health Informatics
    |March 3, 2025
    PubMed
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    This study demonstrates high accuracy in classifying neonatal sleep stages using minimal electroencephalography (EEG) channels and advanced feature selection. The findings support practical Internet of Medical Things (IoMT) applications for infant sleep monitoring.

    Area of Science:

    • Biomedical Engineering
    • Computational Neuroscience
    • Medical Informatics

    Background:

    • Minimal electroencephalography (EEG) channel usage is crucial for Internet of Medical Things (IoMT) applications in neonatal care.
    • Single-channel and edge-based features reduce data transfer and processing, enhancing cost-effectiveness for neonatal sleep analysis.

    Purpose of the Study:

    • To evaluate the efficacy of a single EEG channel for neonatal sleep stage classification.
    • To assess a binary classification scheme for distinguishing awake, sleep states, and transitions to quiet sleep in neonates.

    Main Methods:

    • Extracted 490 features using discrete and continuous wavelet transforms (DWT, CWT), spectral, and temporal analysis from neonatal EEG data.
    • Employed a hybrid univariate and ensemble feature selection approach with multidomain feature fusion.

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  • Developed a stacking-based ensemble classifier using ExtraTree, Random Forest, and Artificial Neural Network (ANN) models.

    • Main Results:

    • Achieved high classification accuracies: 90.37% (sleep/awake), 91.13% (quiet sleep/non-quiet sleep), and 94.88% (quiet sleep/awake).
    • Reported significant Kappa values, indicating strong agreement: 77.5%, 80.29%, and 89.76% respectively.
    • Validated model performance using K-fold and leave-one-subject cross-validation.

    Conclusions:

    • A minimal EEG channel approach is effective for accurate neonatal sleep stage classification.
    • The proposed feature selection and stacking ensemble model demonstrate superior performance for IoMT-based infant sleep monitoring.
    • This research offers a practical and cost-effective solution for continuous neonatal sleep assessment.