A machine learning model for predicting post-stroke epilepsy risk by integrating multimodal EEG-fMRI and clinical biomarkers

An RF model integrating multimodal data was successfully developed to effectively predict PSE risk. EDF, NIHSS score, SLV, BEDWR, and serum NSE were identified as core predictive indicators.

Authors

Publication Details

Journal
Frontiers in Neurology
Published
2026-02-17
DOI
https://doi.org/10.3389/fneur.2026.1722475
Primary Topic
Epilepsy research and treatment
Type
article
Field-Weighted Citation Impact
0.00
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article

A machine learning model for predicting post-stroke epilepsy risk by integrating multimodal EEG-fMRI and clinical biomarkers

Ze Wang, Teng Ma, Huanhuan Liu
Frontiers in Neurology
Epilepsy research and treatment
article

A machine learning model for predicting post-stroke epilepsy risk by integrating multimodal EEG-fMRI and clinical biomarkers

Ze Wang, Teng Ma, Huanhuan Liu
article en

Abstract

An RF model integrating multimodal data was successfully developed to effectively predict PSE risk. EDF, NIHSS score, SLV, BEDWR, and serum NSE were identified as core predictive indicators.

Frontiers in NeurologyVol. 17
Openalex Percentile: Top 56%
Epilepsy research and treatment
DOIOpen Access PDFOpenAlex
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A machine learning model for predicting post-stroke epilepsy risk by integrating multimodal EEG-fMRI and clinical biomarkers — Ze Wang, Teng Ma, et al. · Frontiers in Neurology (2026) | TGRS Research Map | TGRS