Advancements in epilepsy classification: Current trends and future directions.

Area of Science:

• Neurology
• Biomedical Engineering
• Artificial Intelligence

Background:

• Epilepsy classification using Electroencephalogram (EEG) signals faces challenges.
• Machine learning (ML) and deep learning (DL) offer advanced solutions.
• Accurate categorization of focal and non-focal epilepsy is crucial for treatment.

Purpose of the Study:

• To provide a comprehensive survey on categorizing focal and non-focal epilepsy using EEG signals.
• To emphasize the role of ML and DL in overcoming classification challenges.
• To synthesize cutting-edge techniques and propose novel directions for improved classification precision.

Main Methods:

• Review of recent advances in ML and DL methodologies for EEG signal analysis.
• Focus on hybrid models combining signal processing with ML algorithms.
• Analysis of current challenges and potential solutions in epilepsy classification.

Main Results:

• ML and DL significantly enhance the accuracy of epilepsy classification from EEG.
• Hybrid models show promise in integrating traditional and advanced techniques.
• Identified key breakthroughs and limitations in current classification methods.

Conclusions:

• AI-driven approaches, particularly hybrid models, offer improved epilepsy classification.
• Future research should focus on multimodal data integration and real-time seizure prediction.
• Potential for AI to enable personalized epilepsy treatment strategies.