Evidence map›Paper›PMID 42677172›Full record

ArticleInternational IEEE/EMBS Conference on Neural Engineering : [proceedings]. International IEEE EMBS Conference on Neural Engineering2025

Integrating Spatial-Temporal Channel Reliability with AI for Robust High-Frequency Oscillation Detection in Intracranial EEG.

Behrang Fazli Besheli, Amir Hossein Ayyoubi, Chandra Prakash Swamy, Bobby Mohan, Kai J Miller, Jonathan J Parker, Gregory A Worrell, Nuri F Ince

Abstract read
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Article in International IEEE/EMBS Conference on Neural Engineering : [proceedings]. International IEEE EMBS Conference on Neural Engineering, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

8 authors.

Behrang Fazli BesheliDepartment of Neurologic Surgery, Mayo Clinic, Rochester, MN, USA, 55901.
Amir Hossein AyyoubiBioinformatics and Computational Biology Department, University of Minnesota, Minneapolis, MN, USA.
Chandra Prakash SwamyDepartment of Neurologic Surgery, Mayo Clinic, Rochester, MN, USA, 55901.
Bobby MohanDepartment of Neurosurgery, Mayo Clinic, Scottsdale, AZ.
Kai J MillerDepartment of Neurologic Surgery, Mayo Clinic, Rochester, MN, USA, 55901.
Jonathan J ParkerDepartment of Neurosurgery, Mayo Clinic, Scottsdale, AZ.
Gregory A WorrellDepartment of Neurology, Mayo Clinic, Rochester, MN, USA.
Nuri F InceDepartment of Neurologic Surgery, Mayo Clinic, Rochester, MN, USA, 55901.

Funding

Reliable Seizure Prediction Using Physiological Signals and Machine LearningR01NS092882 · NINDS · MAYO CLINIC ROCHESTER · PI Gregory A Worrell · 2015 to 2026
$5.7M
Acute Modulation of Stereotyped High Frequency Oscillations with a Closed-Loop Brain Interchange System in Drug Resistant EpilepsyUH3NS117944 · NINDS · UNIVERSITY OF HOUSTON · PI Nuri Firat Ince · 2021 to 2026
$2.9M
Investigation of Stereotyped High-Frequency Oscillations with Computational Intelligence for the Prediction of Seizure Onset Zone in EpilepsyR01NS112497 · NINDS · UNIVERSITY OF HOUSTON · PI INCE, NURI FIRAT · 2019 to 2023
$2.3M
NINDS NIH HHS R01 NS092882NINDS NIH HHS R01 NS112497NINDS NIH HHS UH3 NS117944
6 · The paper itself

Abstract

Objective: Reliable detection of high-frequency oscillations (HFOs) in intracranial EEG (iEEG) is critical for localizing the seizure onset zone (SOZ). However, artifact contamination severely impairs detection specificity and contributes to false positives. Methods: We developed a reliability guided HFO analysis pipeline that targets artifact elimination through a multi-level strategy, including event level denoising, channel signal quality assessment, and segment level temporal reliability analysis. The classifier was trained on 22 subjects using manual annotations of reliable versus unreliable data and validated using leave-one-subject-out cross-validation (LOSO-CV). Results: The final pipeline was further applied to an additional cohort of 8 independent subjects to evaluate generalizability and clinical utility. SOZ localization accuracy based on HFOs retained after event-level denoising improved from 66% (baseline) to 82%, and further to 93% after applying channel and segment reliability filters. ROC analysis confirmed that reliability-based filtering enhanced separation between SOZ and non-SOZ channels, improving clinical specificity. Conclusion: Incorporating spatial and temporal contextual information, specifically by evaluating signal quality across neighboring channels and consistency across adjacent time windows, significantly enhances the ability to distinguish true HFOs from artifacts. This approach improves both the robustness and clinical accuracy of HFO-based SOZ localization.

Indexed as

EpilepsyHigh Frequency OscillationiEEGNeural Signal ProcessingSpatial PCA

Identifiers

PMID42677172
PMCPMC13527899

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.