Epileptic Seizures Detection Using Deep Learning Techniques: A Review.

Area of Science:

• Neurology
• Artificial Intelligence
• Medical Imaging

Background:

• Epileptic seizure diagnosis traditionally relies on manual feature extraction from EEG and MRI, limiting performance.
• Deep learning (DL) automates feature extraction and classification, significantly advancing medical diagnostics.
• Automated seizure detection using DL with neuroimaging is a rapidly evolving field.

Purpose of the Study:

• To provide a comprehensive overview of deep learning techniques for automated epileptic seizure detection.
• To analyze DL-based rehabilitation systems for epilepsy management.
• To discuss challenges, advantages, limitations, and future directions in DL for seizure diagnosis.

Main Methods:

• Review of existing literature on DL applications in epileptic seizure detection using EEG and MRI.
• Analysis of DL algorithms, feature extraction automation, and classification processes.
• Examination of rehabilitation systems incorporating cloud computing and hardware for DL implementation.

Main Results:

• DL significantly enhances automated epileptic seizure detection accuracy compared to conventional methods.
• Various DL models show promise for improved diagnosis and personalized rehabilitation.
• Key challenges include data heterogeneity, computational demands, and clinical integration.

Conclusions:

• Deep learning offers a powerful, automated approach to epileptic seizure detection and diagnosis.
• Further research is needed to address challenges and optimize DL models for clinical practice.
• Future work should focus on developing robust, integrated DL systems for epilepsy management.