Issues and Causal Factors in Machine Learning Methods for Predicting Epileptic Seizures Using EEG: Towards Clinically Viable Prediction via Utility–Latency Feasibility Constraints
Review explores machine learning challenges in seizure prediction, suggesting improvements in efficiency and interpretability for drug-refractory epilepsy.
Key Points
The review identifies challenges in machine learning methods for predicting epileptic seizures using EEG.
Review of existing research on EEG-based seizure prediction
Discussion on dataset design, feature extraction, and classification methods
Proposal of a classification for pipeline and non-pipeline challenges
Highlights issues with current machine learning approaches for seizure prediction
Emphasizes the need for efficiency in algorithms
Suggests frameworks for comparing machine learning methods