Neonatal Seizure Detection

Neonatal seizure detection aims to develop automated systems for rapidly and accurately identifying seizures in newborns using electroencephalography (EEG), addressing the limitations of current manual methods which require specialized expertise and are time-consuming. Research heavily utilizes deep learning, particularly convolutional neural networks (CNNs) and transformer-based architectures, often incorporating techniques to improve model interpretability and reduce computational demands for deployment on resource-constrained devices. Successful models are demonstrating performance comparable to or exceeding human experts, paving the way for improved diagnosis and treatment of neonatal seizures, potentially reducing long-term neurological damage.