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arXiv Machine Learning By Daniel Wendelken (University of Cincinnati, Cincinnati, USA), Brian Ervin (Cincinnati Children's Hospital Medical Center, Cincinnati, USA), Ravindra Arya (Cincinnati Children's Hospital Medical Center, Cincinnati, USA), Ali A. Minai (University of Cincinnati, Cincinnati, USA)

Graph-Based Approaches to Learning Epileptogenic Zone Localization Using Stereo-EEG Recordings

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The study evaluates how different graph topologies influence the localization of the epileptogenic zone (EZ) from resting‑state stereo‑EEG recordings in 40 patients. Using a consistent learning model and leave‑one‑patient‑out validation, the authors compare dense graphs, anatomy‑ and geometry‑informed priors, budgeted sparsification, learned sparsification, and a new Region‑Bridge‑c topology. At about 30% edge retention, Region‑Bridge‑c achieves the highest mean PR‑AUC and ROC‑AUC while using roughly 69% fewer edges than a dense graph, indicating that graph construction significantly impacts EZ localization performance.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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