Artificial intelligence in electroencephalography analysis for epilepsy diagnosis and management

Area of Science:

• Neurology
• Medical Imaging
• Artificial Intelligence

Background:

• Epilepsy diagnosis relies on electroencephalography (EEG), but manual interpretation is inefficient and prone to errors.
• Traditional EEG analysis faces challenges in accuracy and efficiency for epilepsy management.

Purpose of the Study:

• To systematically review the integration of artificial intelligence (AI), deep learning (DL), and machine learning (ML) in EEG analysis for epilepsy.
• To evaluate AI-EEG models for supportive and predictive applications in epilepsy care.

Main Methods:

• Systematic evaluation of AI, DL, and ML applications in EEG analysis for epilepsy.
• Focus on supportive AI (clinical decision augmentation) and predictive AI (seizure/outcome anticipation).

Main Results:

• AI-based EEG analysis shows potential for improved epilepsy detection, monitoring, and treatment evaluation.
• Key advancements include enhanced precision, efficiency, multimodal data fusion, and personalized diagnosis.
• Challenges include limited interpretability, data quality issues, and clinical translation barriers.

Conclusions:

• AI-EEG offers transformative potential for epilepsy care but requires clinician verification and multidimensional clinical data.
• Future research should focus on algorithm optimization, data quality, AI transparency, and interdisciplinary collaboration for clinical implementation.