Seizure Detection

Seizure detection research aims to develop accurate and efficient methods for identifying epileptic seizures from various data sources, primarily electroencephalograms (EEGs) but also including video and other physiological signals. Current research heavily utilizes deep learning, employing convolutional neural networks (CNNs), transformers, recurrent neural networks (RNNs), and graph neural networks (GNNs) to analyze complex spatiotemporal patterns in the data, often incorporating techniques like transfer learning and self-supervised learning to improve model performance and generalization. Successful advancements in this field have significant implications for improving patient care, enabling earlier interventions, and potentially leading to more personalized treatment strategies for epilepsy.