EEG-Based Seizure Prediction Approaches Within Clinically Relevant Pre-seizure Windows Using Scalp EEG Datasets: A...

Area of Science:

• Neuroscience
• Artificial Intelligence
• Medical Technology

Background:

• Epilepsy affects millions globally, necessitating advanced seizure prediction methods.
• Scalp electroencephalography (EEG) is a key modality for monitoring brain activity.
• Current seizure prediction models face challenges in clinical translation.

Purpose of the Study:

• To systematically review deep learning and machine learning models for EEG-based seizure prediction.
• To assess predictive performance, methodological heterogeneity, and validation strategies.
• To identify factors influencing the clinical utility of seizure prediction systems.

Main Methods:

• Systematic review of studies using deep learning and machine learning for scalp EEG seizure prediction.
• Analysis of predictive performance metrics (sensitivity, ROC curves, false alarms).
• Evaluation of methodological variations (preictal window, prediction horizon, occurrence period) and validation approaches.

Main Results:

• Significant variability in reported performance across studies.
• Limited external validation and inconsistent reporting of false alarms.
• Models show potential for moderate-to-high sensitivity but lack robust real-world generalizability.

Conclusions:

• Clinical translation of EEG seizure prediction requires standardized methodologies and rigorous validation.
• Addressing methodological heterogeneity and alarm burden is crucial for reliable epilepsy management.
• Prospective, real-world evaluations are needed to confirm clinical utility.