Artificial Intelligence Techniques for An Optimized Learning Network based Ictal and Interictal States of Automatic Seizure Detection Using Multi-Channel Scalp EEG: Trends and Challenges

Abstract

Automatic seizure detection using multi-channel scalp electroencephalogram (EEG) signals has emerged as a critical research domain due to its potential to support clinical diagnosis and continuous patient monitoring. The differentiation between ictal and interictal states remains a challenging task due to the complex, non-linear, and highly variable nature of EEG signals. In recent years, artificial intelligence techniques, particularly deep learning and optimized learning networks, have demonstrated significant promise in enhancing detection accuracy and robustness. This study presents a comprehensive analysis of artificial intelligence-driven approaches for seizure detection, focusing on optimized learning frameworks that integrate feature extraction, temporal modeling, and optimization strategies. The paper explores various machine learning and deep learning architectures, including convolutional neural networks, recurrent neural networks, hybrid models, and attention-based mechanisms, along with optimization techniques such as evolutionary algorithms and hyperparameter tuning. Furthermore, it highlights emerging trends such as end-to-end learning, multimodal integration, and real-time deployment. Despite notable advancements, several challenges persist, including data imbalance, generalization issues, interpretability, and computational constraints. This study aims to provide a structured overview of current methodologies, identify research gaps, and outline future directions for developing reliable and scalable seizure detection systems using optimized artificial intelligence techniques.