Machine Learning for Predicting Epileptic Seizures Using EEG Signals: A Review

Area of Science:

• Biomedical Engineering
• Clinical Neuroscience
• Artificial Intelligence in Medicine

Background:

• Epilepsy management requires accurate prediction of unpredictable seizures for timely intervention.
• Current seizure prediction methods face challenges due to data limitations and complexity.
• Advancements in artificial intelligence (AI) and machine learning (ML) present opportunities to improve early seizure detection.

Purpose of the Study:

• To comprehensively review state-of-the-art ML techniques for early seizure prediction using electroencephalogram (EEG) signals.
• To identify current gaps, challenges, and pitfalls in ML-based seizure prediction research.
• To recommend future research directions for enhanced epilepsy management.

Main Methods:

• Systematic review of recent literature on ML algorithms applied to EEG data for seizure prediction.
• Analysis of various ML approaches, including deep learning and traditional methods.
• Evaluation of data requirements and preprocessing techniques for seizure prediction models.

Main Results:

• ML algorithms show significant potential for improving the accuracy and timeliness of seizure prediction.
• EEG signal analysis using ML can identify patterns preceding epileptic seizures.
• Data scarcity and model interpretability remain key challenges in the field.

Conclusions:

• ML techniques are poised to revolutionize early seizure prediction, offering a paradigm shift in epilepsy care.
• Further research is needed to address data limitations and enhance the robustness of ML models.
• Improved seizure prediction can significantly alleviate the adverse consequences of epilepsy for patients.