Transformer-Based Wavelet-Scalogram Deep Learning for Improved Seizure Pattern Recognition in Post-Hypoxic-Ischemic Fetal Sheep EEG.

Hypoxic-ischemic (HI) events in newborns can trigger seizures, which are highly associated with later neurodevelopmental impairment. The precise detection of these seizures is a complex task requiring considerable very specialized expertise, underscoring the necessity for automated methods to support diagnosis and therapeutic interventions. We have previously shown the effectiveness of deep-learning algorithms, involving a 17-layer deep convolutional neural network (CNN), to identify and quantify post-HI high-amplitude seizures (HAS) in preterm fetal sheep models. This method, which incorporated 2D wavelet scalogram (WS) images of EEG patterns, achieved a cross-validated overall accuracy of 97.19% (AUC=0.96). In the current study, we now assess the utility of Transformer models for seizure detection in the EEG of pre- and near-term fetal sheep during the first 48 hours of recovery after HI. We trained a series of Transformer models on a smaller subset of the original dataset, comprising 800 WS of EEG seizure and non-seizure patterns. Out of all classifiers, the Visual Transformer (ViT) exceeded the performance of the previous deep CNNs, achieving an overall accuracy of 99.5% with an AUC of 0.995.Clinical relevance-This study showcases the superior performance, efficiency, and robustness of Transformer models for identifying HI-induced EEG seizures, with high potential to be clinical decision support tools to identify neonatal seizures at the cotside.