Self‐supervised data‐driven approach defines pathological high‐frequency oscillations in epilepsy

Our data-driven approach using the generative artificial intelligence model yielded a novel, explainable definition of pathological HFOs, which has the potential to further enhance the clinical use of HFOs for EZ delineation.

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Publication Details

Journal
Epilepsia
Published
2025-07-12
DOI
https://doi.org/10.1111/epi.18545
Citations
4
Primary Topic
Epilepsy research and treatment
Type
article
Field-Weighted Citation Impact
5.88

Funders

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article

Self‐supervised data‐driven approach defines pathological high‐frequency oscillations in epilepsy

Aria Fallah, Eishi Asano, Yuanyi Ding, Atsuro Daida et al.
4 citations
Epilepsia
Epilepsy research and treatment
5.88
article

Self‐supervised data‐driven approach defines pathological high‐frequency oscillations in epilepsy

Aria Fallah, Eishi Asano, Yuanyi Ding, Atsuro Daida, Noriko Salamon, Hiroki Nariai, Shaun A. Hussain, Lawrence Liu, Joe X Qiao, Myung‐Shin Sim, Vwani Roychowdhury, Chenda Duan, Shingo Oana, Naoto Kuroda, Sotaro Kanai, Raman Sankar, Tonmoy Monsoor, Jerome Engel, Richard J. Staba, Yipeng Zhang
article en
4 citations

Abstract

Our data-driven approach using the generative artificial intelligence model yielded a novel, explainable definition of pathological HFOs, which has the potential to further enhance the clinical use of HFOs for EZ delineation.

EpilepsiaVol. 66(11)
Children’s Institute (US), University of California, Los Angeles (US), Wayne State University (US), Children's Hospital of Michigan (US), Olive View-UCLA Medical Center (US), Mattel Children's Hospital (US)
National Institute of Neurological Disorders and Stroke, National Center for Advancing Translational Sciences, Children's Discovery and Innovation Institute, University of California, Los Angeles
Openalex Percentile: Top 4%
Epilepsy research and treatment
5.88
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