A Review of EEG-Based Epileptic Seizure Detection Techniques Using Signal Processing, Neural Networks and Optimization Algorithms

Epileptic seizure detection has become an increasingly important research area due to the rising number of epilepsy cases and the need for reliable, early diagnosis. Electroencephalography (EEG) remains the most widely used tool for identifying epileptic activity because of its non-invasive nature and ability to capture rapid changes in brain electrical signals. Over the years, numerous computational approaches—ranging from classical signal-processing techniques to advanced deep-learning and optimization-driven models—have been developed to improve automatic seizure-detection accuracy. This review presents a comprehensive analysis of 40 peer-reviewed studies published between 2015 and 2024 focusing on EEG-based seizure detection. The examined methods include time- and frequency-domain signal processing, classical machine-learning classifiers, deep-learning architectures such as CNNs, LSTMs, and Transformers, graph neural networks for connectivity modeling, and hybrid approaches enhanced through evolutionary optimization algorithms. The analysis highlights their strengths, limitations, and performance trends across different datasets. The findings indicate that while deep-learning models offer significant improvements in accuracy and automated feature extraction, they still face challenges such as the need for large labeled datasets, high computational requirements, and limited interpretability. Traditional signal-processing and machine-learning methods provide interpretability but lack generalization and scalability. Graph-based models show promise in capturing spatial relationships between EEG channels but require further refinement for real-time use. The review identifies major research gaps, including poor cross-dataset generalization, limited robustness to noise, absence of explainable AI frameworks, lack of multimodal data fusion, and insufficient clinical validation. Based on these gaps, several future research directions are proposed, including lightweight real-time models, patient-specific adaptive systems, multimodal integration, explainable AI, dynamic graph modeling, and cloud/IoT-enabled monitoring platforms. Overall, this review consolidates current trends, challenges, and emerging opportunities in EEG-based seizure detection, supporting the development of next-generation systems that are accurate, reliable, interpretable, and clinically deployable.