Key result
Deep learning on ECG spectrograms outperforms classical heart rate methods for grading neonatal HIE.
Why the study?
Neonates with moderate to severe HIE benefit from therapeutic hypothermia, but ECG signals are more accessible than EEG in the neonatal unit for developing automatic grading systems.
Does a deep learning approach using ECG spectrograms improve the grading of neonatal Hypoxic-Ischemic Encephalopathy compared to classical heart rate feature extraction?
Does a deep learning approach using ECG spectrograms improve the grading of neonatal Hypoxic-Ischemic Encephalopathy compared to classical heart rate feature extraction?
Utilizing newborn ECG spectrograms with deep learning provides a potentially more accessible and accurate method for automatic HIE grading compared to classical feature extraction.
May enhance automated neonatal HIE grading; leaves open prospective validation before clinical adoption.
The grade of Hypoxic-Ischemic Encephalopathy (HIE), a condition caused by cerebral oxygen deprivation, can be determined through analysis of Electroencephalogram (EEG). Neonates with moderate to severe HIE can be treated with therapeutic hypothermia, which has prompted the development of an automatic HIE grading system. Electrocardiogram (ECG) signals are easier and more accessible to obtain than the EEG in the Neonatal Unit and may help grade HIE. This research explores two different approaches for grading HIE, leveraging a large ECG dataset from newborns. The classical approach involves calculating heart rate (HR) from ECG signal followed by feature extraction in both time and frequency domains and classification using Random Forest and multilayer perceptron. In contrast, the proposed approach extracts the spectrograms directly from the ECG signal to serve as input for a convolutional neural network. The results indicate that the proposed method achieves a higher AUC while bypassing the time-consuming process of HR calculation and enabling the use of more robust deep learning models.Clinical relevance- Utilizing newborn ECG signals as an alternative to EEG signals for automatic HIE grading may provide a more accessible method for clinicians to grade encephalopathy and evaluate the need for hypothermia treatment.
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Rezaei et al. (2025) studied Neonatal Hypoxic-Ischemic Encephalopathy (HIE). Deep learning approach using ECG spectrograms (CNN) vs. Classical approach (heart rate feature extraction with Random Forest/MLP) was evaluated on Area under the curve (AUC) for HIE grading. A deep learning approach using ECG spectrograms achieved a higher AUC for grading neonatal hypoxic-ischemic encephalopathy compared to classical heart rate feature extraction methods.
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