# Investigation of Stereotyped High-Frequency Oscillations with Computational Intelligence for the Prediction of Seizure Onset Zone in Epilepsy

> **NIH NIH R01** · UNIVERSITY OF HOUSTON · 2022 · $459,231

## Abstract

PROJECT SUMMARY
Neurosurgical therapy of refractory epilepsy requires accurate localization of seizure onset zone (SOZ). In clinical
practice, intracranial EEG (iEEG) is recorded in the epilepsy monitoring unit (EMU) over many days where
multiple seizures are recorded to provide information to localize the SOZ. The prolonged monitoring in the EMU
adds to the risk of complications and can include intracranial bleeding and potentially death. Recently, high
frequency oscillations (HFO) of iEEG between 80 to 500 Hz are highly valued as a promising clinical biomarker
for epilepsy. HFOs are believed to be clinically significant, and thus could be used for SOZ localization. However,
HFOs can also be recorded from normal and non-epileptic cerebral structures. When defined only by rate or
frequency, pathological HFOs are indistinguishable from physiological ones, which limit their application in
epilepsy pre-surgical planning. In this proposal, to the best of our knowledge, we show of a recurrent waveform
pattern that distinguishes pathological HFOs from physiological ones. In particular, we observed that the SOZ
generates repeatedly a set of stereotyped HFO waveforms whereas the HFOs from nonepileptic regions were
irregular in their waveform morphology. Based on these observations, using computational tools built on recent
advances in sparse coding and unsupervised machine learning techniques, we propose to detect these
stereotyped recurrent HFO waveform patterns directly from the continuous iEEG data of adult and pediatric
patients and test their prognostic value by correlating the spatial distribution of detected events to clinical findings
such as SOZ, resection zone and seizure freedom. We hypothesize that accurate detection of pathologic HFOs
in brief iEEG recordings can identify the SOZ and eliminate the necessity of prolonged EMU monitoring and
reduce the associated risks. With these motivations, in this project an interdisciplinary team composed of
biomedical engineers, epileptologists and neurosurgeons will work together to develop and test novel
computational tools to detect stereotyped HFOs and its subtypes in large iEEG datasets recorded with clinical
electrodes. Developed algorithms and iEEG data will be shared with the research community to contribute to the
reproducible research and help other research groups to develop novel methods. The results of this study will
be essential for achieving our group's long term goal of developing an online neural signal processing system
for the rapid and accurate identification of SOZ with brief invasive recording.

## Key facts

- **NIH application ID:** 10388243
- **Project number:** 5R01NS112497-04
- **Recipient organization:** UNIVERSITY OF HOUSTON
- **Principal Investigator:** Nuri Firat Ince
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $459,231
- **Award type:** 5
- **Project period:** 2019-07-15 → 2024-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10388243

## Citation

> US National Institutes of Health, RePORTER application 10388243, Investigation of Stereotyped High-Frequency Oscillations with Computational Intelligence for the Prediction of Seizure Onset Zone in Epilepsy (5R01NS112497-04). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10388243. Licensed CC0.

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