Deep learning in intracranial EEG for seizure detection: advances, challenges, and clinical applications

Deep learning has emerged as a transformative tool for the automated detection and classification of seizure events from intracranial EEG (iEEG) recordings. In this review, we synthesize recent advancements in deep learning techniques including convolutional neural networks (CNN), recurrent neural networks (RNN) with long short term memory (LSTM) units, and transformer based architectures that enable accurate localization of epileptogenic zones (EZ) in drug resistant epilepsy. These approaches effectively extract spatial and temporal features from raw iEEG signals to detect epileptiform discharges (ED) including seizures alongside other electro-physiological biomarkers such as high-frequency oscillations (HFO). Importantly, beyond relying solely on these traditional markers, several studies have indicated direct seizure detection by modeling ictal and preictal dynamics. Such methods capture alternative biomarkers including spectral changes, connectivity patterns, and complex temporal signatures that directly reflect seizure activity. Although deep learning models often achieve high accuracy, they continue to face several challenges due to data scarcity, heterogeneity in iEEG acquisition, inconsistent preprocessing protocols, and limited model interpretability. We also highlight emerging integrative strategies that combine multimodal neuroimaging data with deep learning analyses as well as neuromorphic computing techniques designed for real-time clinical application. Addressing these limitations has significant potential for surgical planning, reducing diagnostic subjectivity, and ultimately enhancing patient outcomes in epilepsy care.