A Survey of Methods and Architectures for An Optimized Learning Network based Ictal and Interictal States of Automatic Seizure Detection Using Multi-Channel Scalp EEG

Abstract

Automatic seizure detection using multi-channel scalp electroencephalography has become a critical research domain due to its potential to assist neurologists in real-time diagnosis and monitoring of epilepsy. This paper presents a comprehensive survey of methods and architectures designed for optimized learning networks that distinguish between ictal and interictal states. Traditional machine learning techniques, including feature-based classifiers, have been widely explored but often suffer from limitations in scalability and generalization. Recent advances in deep learning, including convolutional neural networks, recurrent neural networks, and hybrid architectures, have significantly improved detection accuracy by enabling end-to-end learning from raw EEG signals. Optimization strategies such as attention mechanisms, evolutionary algorithms, and hyperparameter tuning further enhance model performance and robustness. This survey systematically reviews existing approaches, highlighting their methodologies, datasets, architectural designs, and performance metrics. The study also discusses challenges such as data imbalance, noise sensitivity, computational complexity, and lack of interpretability. Furthermore, emerging trends including explainable artificial intelligence, transfer learning, and edge-based deployment are examined. The objective of this paper is to provide a consolidated understanding of current advancements and identify future research directions for developing efficient and reliable seizure detection systems.