REVIEW article
Front. Neurosci.
Sec. Neural Technology
Volume 19 - 2025 | doi: 10.3389/fnins.2025.1677898
Deep Learning in Intracranial EEG for Seizure Detection: Advances, Challenges, and Clinical Applications
Provisionally accepted- Nazarbayev University, Astana, Kazakhstan
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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.
Keywords: clinical neurophysiology, High frequency oscillations (HFO), intracranial EEG (iEEG), Epileptiform discharges, MachineLearning in Healthcare, deep learning, Neural signal analysis, Signal processing
Received: 01 Aug 2025; Accepted: 06 Oct 2025.
Copyright: © 2025 Qamar, Lee and Abibullaev. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Berdakh Abibullaev, berdakh.abibullaev@nu.edu.kz
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