Seizure Prediction

Epileptic seizure prediction aims to improve patient safety and quality of life by providing advance warning of impending seizures. Current research heavily utilizes deep learning models, including convolutional neural networks (CNNs), recurrent neural networks (RNNs, such as LSTMs), and transformer architectures, often applied to electroencephalogram (EEG) data, sometimes incorporating other sensor modalities like electrocardiograms (ECGs) or brain MRI data. These models are being refined through techniques like data augmentation, improved feature extraction methods, and the exploration of both supervised and unsupervised learning paradigms to enhance prediction accuracy and reduce false positives. Successful implementation of these methods holds significant promise for personalized seizure management and reducing the burden of this neurological disorder.