Deep Learning Approaches For Epileptic Seizures Detection And Prediction Through EEG Signals: A Review

Abstract

This literature review delves into the advancements in epileptic seizure detection utilizing Convolutional Neural Networks (CNNs) and related deep learning techniques. It covers a wide array of research, from basic CNN models to complex hybrid approaches incorporating multi-modal data, transfer learning, and real-time processing. The review underscores the high accuracy, robustness, and applicability of CNN-based models in seizure detection tasks, with enhancements achieved through innovative methods such as spectrograms, wavelet transforms, and multi-channel EEG data. Nevertheless, difficulties such as the computational demands of deep learning models, the need for large and diverse datasets, and generalization across different patient groups and seizure types persist. Additionally, the real-time application of these models, particularly in resource-constrained environments, requires further exploration. The review wraps up by pointing out the advantages and limitations of current approaches and suggests future research directions, including the combination of responsible AI and the development of lightweight models for portable devices.