Machine learning in epilepsy.

Area of Science:

• Neurology
• Computational Neuroscience
• Biomedical Data Science

Background:

• Epilepsy involves complex, multi-scale pathological processes.
• Traditional analysis methods struggle with high-dimensional, nonlinear electrophysiological data.
• Machine learning (ML) offers powerful analytical tools but faces interpretability challenges.

Purpose of the Study:

• To review ML methods in epilepsy research across cellular, network, and multiscale levels.
• To examine inferential assumptions, methodological pitfalls, and interpretability of ML applications.
• To highlight ML's role in advancing epilepsy phenotyping, seizure detection/prediction, and modeling.

Main Methods:

• Review of unsupervised learning for cellular phenotyping.
• Review of supervised learning for electroencephalogram (EEG)-based seizure detection and prediction.
• Examination of multiscale modeling frameworks integrating neuronal and network dynamics.

Main Results:

• Unsupervised ML identifies latent excitability phenotypes.
• Supervised ML improves automated seizure detection and prediction, despite challenges.
• ML-driven multiscale models offer a path to clinically actionable epilepsy insights.

Conclusions:

• ML enhances epilepsy research by bridging data analysis and neurophysiological theory.
• Prioritizing interpretability and cross-scale integration is crucial for clinical translation.
• ML-enhanced multiscale frameworks promise more interpretable and actionable epilepsy models.