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A Deep Learning Approach to Grading Neonatal Hypoxic-Ischemic Encephalopathy Using ECG Spectrograms.

Kimia Rezaei, Sean R Mathieson, Gordon Lightbody

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |December 3, 2025
    PubMed
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    Newborn ECG signals offer a simpler way to grade Hypoxic-Ischemic Encephalopathy (HIE). A deep learning approach using ECG spectrograms shows promise for automatic HIE assessment and guiding hypothermia treatment.

    Area of Science:

    • Biomedical Engineering
    • Neonatal Neurology
    • Computational Medicine

    Background:

    • Hypoxic-Ischemic Encephalopathy (HIE) is a serious condition in newborns caused by oxygen deprivation to the brain.
    • Accurate grading of HIE is crucial for determining eligibility for therapeutic hypothermia.
    • Electroencephalogram (EEG) is currently used for HIE grading, but Electrocardiogram (ECG) signals are more accessible in Neonatal Units.

    Purpose of the Study:

    • To explore and compare two distinct methods for automatic HIE grading using neonatal ECG signals.
    • To evaluate the efficacy of a novel deep learning approach utilizing ECG spectrograms against a classical feature extraction method.
    • To establish ECG as a viable and accessible alternative to EEG for HIE assessment.

    Main Methods:

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  • A large dataset of newborn ECG signals was utilized.
  • Classical approach: Heart Rate (HR) calculation, time/frequency domain feature extraction, and classification (Random Forest, multilayer perceptron).
  • Proposed approach: Direct extraction of spectrograms from ECG signals for input into a convolutional neural network.

    • Main Results:

    • The proposed deep learning method achieved a higher Area Under the Curve (AUC) compared to the classical approach.
    • The convolutional neural network approach bypassed the need for time-consuming HR calculation.
    • Deep learning models demonstrated greater robustness in grading HIE from ECG data.

    Conclusions:

    • ECG signals can be effectively used for automatic HIE grading, offering a more accessible alternative to EEG.
    • The deep learning approach using ECG spectrograms is a promising and efficient method for HIE assessment.
    • This technique may enhance clinical decision-making regarding therapeutic hypothermia for neonates with HIE.