AI-Driven Mapping of Seizure Spread Patterns.

Abstract

Objective: The focus of epilepsy research has largely been on seizure onset; however, physicians typically examine the patterns of seizure spread past seizure onset as well. This study aims to align automated seizure analysis with clinical practice, leverage deep learning to standardize seizure annotations that varies among physicians, and understand common seizure spread patterns across patients.

Methods: We developed deep learning algorithms on a small subset of patients to detect seizure activity and deployed these algorithms across 275 seizures in 71 patients to analyze the patterns of seizure spread (extent, timing, surgical outcomes, and common patterns) along with incorporating diffusion-weighted imaging to understand how these patterns relate to the structural connections of the brain.

Results: Deep learning algorithms outperform single features (line length, absolute slope, and power) in ranking seizure onset contacts using physician annotations as a benchmark. We also find that poor outcome patients have more extensive brain regions involved in their seizures while also having more rapid spread between temporal lobes. Incorporating diffusion-weighted imaging, we find that an increase in structural connectivity between temporal lobes is associated with quicker seizure spread. Finally, we identify clusters of spread patterns common across patients based on spread timing, location, and extent.

Interpretation: Analyzing seizure spread can reveal new insights into seizure evolution and its relationship with surgical outcomes in patients with epilepsy. The findings also suggest that focusing beyond seizure onset is crucial for understanding and treating epilepsy. ANN NEUROL 2026.