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Updated: Feb 28, 2026

Interictal High Frequency Oscillations Detected with Simultaneous Magnetoencephalography and Electroencephalography as Biomarker of Pediatric Epilepsy
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Predicting Surgical Outcome in Drug-Resistant Epilepsy by Combining Interictal Biomarkers within a Machine Learning

Hmayag Partamian1, Saeed Jahromi1, M Scott Perry1

  • 1Cook Children's Health Care System.

Research Square
|February 27, 2026
PubMed
Summary

This study introduces a machine-learning approach using interictal spikes and ripples to better identify the epileptogenic zone (EZ) in drug-resistant epilepsy (DRE) patients, improving surgical outcomes.

Keywords:
combined interictal featuresepilepsy surgeryinterictal epilepsy biomarkerssupervised machine learning

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Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Computational Biology

Background:

  • Accurate delineation of the epileptogenic zone (EZ) is crucial for successful epilepsy surgery in drug-resistant epilepsy (DRE).
  • Traditional methods relying on seizure onset from ictal intracranial EEG (iEEG) are challenging to acquire.
  • Interictal iEEG abnormalities are abundant but lack specificity for EZ identification.

Purpose of the Study:

  • To develop and validate a machine-learning framework integrating interictal spike and ripple features for automated EZ delineation.
  • To assess the framework's performance in predicting surgical outcomes compared to individual biomarkers.
  • To improve the precision and prognostic value of EZ localization in pediatric DRE.

Main Methods:

  • Retrospective analysis of iEEG data from 62 pediatric DRE patients undergoing neurosurgery.
  • Automated detection and feature extraction (temporal, spectral, spatial) of interictal spikes and ripples.
  • Training Random Forest classifiers to predict EZ and surgical outcomes using combined and individual features.

Main Results:

  • The combined spike-ripple feature model achieved an AUC of 0.9 for EZ delineation, outperforming individual biomarkers.
  • The model demonstrated 74% spatial overlap with the resected area.
  • The integrated feature model showed the best outcome prediction performance (sensitivity 88%, specificity 68%, accuracy 79%).

Conclusions:

  • Integrating multimodal interictal EEG features significantly enhances EZ delineation accuracy in DRE.
  • This machine-learning approach offers valuable prognostic insights for epilepsy surgery planning.
  • The framework provides a more reliable method for identifying epileptogenic tissue compared to conventional techniques.