Deep Learning Approaches for EEGBased Seizure Prediction in PediatricEpilepsy: Current Advances, Challenges, and Future Directions

Abstract

Pediatric epilepsy is one of the most prevalent neurological disorders in children and is associated with significant cognitive, developmental, and psychosocial consequences. The unpredictable nature of seizures poses substantial challenges for patients, caregivers, and clinicians, emphasizing the need for accurate seizure prediction systems. Electroencephalography (EEG) remains the gold standard tool for seizure monitoring due to its high temporal resolution and ability to capture epileptiform activity. In recent years, deep learning approaches have demonstrated considerable potential in improving EEG driven seizure prediction by automatically extracting complex spatiotemporal patterns from neural signals. This review summarizes current advances in deep learning-based seizure prediction in pediatric epilepsy, focusing on convolutional neural networks (CNNs), recurrent neural networks (RNNs), long short-term memory (LSTM) networks, hybrid CNN-LSTM architectures, and transformer-based models. The review also discusses pediatric specific EEG characteristics, signal pre-processing methods, feature extraction techniques, and benchmark datasets commonly used for model evaluation. Although hybrid and transformer-based models have achieved promising predictive accuracies, several challenges continue to hinder clinical translation, including limited pediatric specific EEG datasets, age dependent EEG variability, poor cross patient generalizability, black box model interpretability, wearable device limitations, and ethical concerns regarding pediatric data privacy. Emerging research directions such as explainable artificial intelligence, multimodal biomarker integration, federated learning, wearable EEG technologies, and edge AI systems are also highlighted. Overall, deep learning driven seizure prediction holds substantial promise for personalized pediatric epilepsy management and may significantly improve long term neurological outcomes through early intervention and real time monitoring.