Advances in machine learning for epileptic seizure prediction: A review of electrocardiogram-based approaches

Key Points

• Machine learning techniques significantly enhance seizure prediction capabilities, indicating their potential utility in clinical settings.
• The review emphasizes the use of classification algorithms and feature extraction to improve prediction accuracy in electrocardiogram data.
• Observational analysis of various algorithm approaches emphasizes their effectiveness across multiple datasets and applications.
• Current findings suggest a need for further validation in real-world clinical environments to establish generalizability.