Brain Waves Unleashed: Illuminating Neonatal Seizure Detection via Multi-scale Hierarchical Modeling

Neonatal seizures are a prevalent clinical manifestation of neurological disorders and can potentially impact the neurodevelopment of the infant’s brain. Accurate and timely detection of neonatal epilepsy is crucial for early diagnosis and treatment. However, because of the complexity of newborn brains and signal instability, current seizure detection methods often produce false positives and negatives. In this work, we propose an automated scheme for detecting neonatal seizures, namely MSHENet. Through a multi-scale hierarchical modeling approach, the model constructs deep learning models at various levels, including the time dimension, channel dimension, and local details, to differentiate between normal brain electrical activity and epileptic seizures. Experiments conducted on a comprehensive real public neonatal electroencephalogram (EEG) dataset demonstrate that our method achieves higher accuracy in detection and a lower rate of false positives and negatives.