Deep Learning and Optimization Approaches in an Optimized Learning Network based Ictal and Interictal States of Automatic Seizure Detection Using Multi-Channel Scalp EEG: A Review

Abstract

Automatic seizure detection using electroencephalogram (EEG) signals has emerged as a critical area of research in biomedical signal processing and clinical neurology. The differentiation between ictal and interictal states plays a significant role in improving diagnostic accuracy and real-time monitoring of epilepsy. Traditional machine learning approaches often struggle with nonlinear patterns and high-dimensional EEG data, necessitating the adoption of deep learning and optimization techniques. This review presents a comprehensive analysis of optimized learning networks, including convolutional neural networks, recurrent neural networks, and hybrid architectures, for multi-channel scalp EEG-based seizure detection. Emphasis is placed on preprocessing strategies, feature extraction mechanisms, and end-to-end learning frameworks that enhance model robustness and generalization. Optimization methods such as evolutionary algorithms, hyperparameter tuning, and attention mechanisms are explored for improving classification performance. The study also evaluates publicly available datasets and performance metrics such as accuracy, sensitivity, specificity, and F1-score. Furthermore, the integration of deep learning models into clinical decision support systems is discussed. This review highlights current advancements, challenges, and future directions in the development of efficient and scalable seizure detection systems, contributing to improved patient care and neurological assessment.