Validation of an Automated Seizure Detection Procedure for Multi-Channel Neonatal EEG

This study validates an automated seizure detection algorithm for multi-channel neonatal EEG, adapting a previously published method to a dataset with fewer electrodes. The Python-based implementation, SDApy, was applied to EEG recordings from 23 neonates to classify seizure and non-seizure epochs using a support vector machine trained on an independent dataset. The algorithm employs time- and frequency-domain features and maintains high generalization across different recording setups, achieving robust performance despite using only nine electrodes instead of nineteen. Evaluation metrics, including F1 scores and precision—recall curves, confirmed strong agreement between algorithm predictions and expert annotations for most patients. SDApy’s open-source implementation enhances accessibility compared with earlier MatLab versions, offering a transparent and cost-effective approach to clinical EEG analysis. The pipeline can operate with labels from a single expert, supports data pre-labeling for deep learning, and integrates well into neonatal intensive care unit monitoring workflows. Overall, SDApy demonstrates reliable adaptation to reduced-channel EEG and shows potential for real-time seizure detection, personalized threshold optimization, and integration into multimodal neurophysiological monitoring systems.