Evidence map›Paper›PMID 42348802›Full record

ArticleNeurology2026

The Influence of Recording Duration and Vigilance State on High-Frequency Oscillation Characterization in Epilepsy.

Zhuying Chen, Stephen V Gliske, Abdallah Alsammani, Kevin Tyner, Srijita Das, Moustafa Mutahr, Jack Lin, Garnett Hegeman, Garnett Smith, David B Grayden and 1 more

Abstract read
In one paragraph

Article in Neurology, 2026. 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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4 · The record

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5 · Who and what money

Authors and funding

11 authors.

Zhuying ChenDepartment of Biomedical Engineering and Graeme Clark Institute, University of Melbourne, Australia.ORCID 0000-0002-0997-2004
Stephen V GliskeDepartment of Neurosurgery, University of Nebraska Medical Center, Omaha.
Abdallah AlsammaniDepartment of Neurosurgery, University of Nebraska Medical Center, Omaha.ORCID 0000-0003-4340-4550
Kevin TynerDepartment of Neurosurgery, University of Nebraska Medical Center, Omaha.
Srijita DasDepartment of Neurosurgery, University of Nebraska Medical Center, Omaha.ORCID 0009-0003-8406-6487
Moustafa MutahrElectrical Engineering and Computer Science Department, University of Michigan, Ann Arbor.ORCID 0009-0003-6669-1369
Jack LinDepartment of Neurology, University of Michigan, Ann Arbor.
Garnett HegemanDepartment of Neurology, University of Michigan, Ann Arbor.
Garnett SmithDepartment of Pediatrics, University of Michigan, Ann Arbor; and.
David B GraydenDepartment of Biomedical Engineering and Graeme Clark Institute, University of Melbourne, Australia.
William StaceyDepartment of Neurology, University of Michigan, Ann Arbor.ORCID 0000-0002-8359-8057

Funding

Characterizing High Frequency Oscillations as an epilepsy biomarker with Big Data toolsR01NS094399 · NINDS · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI STACEY, WILLIAM CHARLES · 2015 to 2025
$4.1M
Epileptic biomarkers and big data: identifying brain regions to resect in patients with refractory epilepsyK01ES026839 · NIEHS · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI GLISKE, STEPHEN V · 2015 to 2019
$734k
NIEHS NIH HHS K01 ES026839NINDS NIH HHS R01 NS094399
6 · The paper itself

Abstract

BACKGROUND AND

objectivesHigh-frequency oscillations (HFOs) are a promising biomarker for localizing epileptogenic tissue, yet the extent to which recording duration and vigilance state influence their spatial distributions remains unclear. This study quantified the recording duration and vigilance state required to reliably capture HFO spatial distributions to guide surgical planning in epilepsy.

methodsWe retrospectively analyzed long-term, continuous iEEG recordings from patients with drug-resistant epilepsy undergoing presurgical evaluation at the University of Michigan. Sleep stages were manually annotated, HFOs (80-500 Hz) were detected using a validated algorithm, and the correlations between HFOs and the seizure-onset zone (SOZ) were assessed across vigilance states. A novel similarity-based temporal padding approach was developed to measure the similarity of HFO distributions derived from data available up to a given time point with those derived from the full recording. Postsurgical outcome prediction was evaluated using a decision tree classifier based on the proportion of resected, top-ranked HFO-rate channels (critical resection percentage, CReP).

resultsFifty-four patients were analyzed (30 female patients, mean age 32.8 [range, 6-66] years, mean recording duration 8.40 days [range, 3-22]). HFO-SOZ associations were present across all states but fluctuated over time; non-rapid eye movement (NREM) sleep showed the least temporal variability. HFO-SOZ associations were stronger in temporal lobe epilepsy and more stable in patients with frequent seizures. When analyzing only the HFOs during NREM, 21% of patients required more than 2 days of recording to capture its full distribution; however, all NREM data were insufficient to describe the full HFO distribution in 30% of patients. Across all vigilance states, 7 days of recording fully characterized HFO distributions in 98% of patients. Postsurgical outcome prediction using multiday aggregate CReP achieved more robust and accurate performance (AUC 0.86, 95% CI [0.70-1.00]) than analyses based on a random selection of single 5-minute NREM epoch (median AUC 0.42, IQR 0.23) or a single day (AUCs ranged 0.63-0.71 for different analyzed days). DISCUSSION: Short sampling period risks incomplete representation of the full HFO spatial profile. Future studies should consider multiday recordings to enable more reliable HFO characterization and improve HFO-based surgical outcome prediction.

Indexed as

ArousalBrain WavesDrug Resistant EpilepsyEpilepsyAdolescentAdultAgedChildElectroencephalographyFemaleHumansMaleMiddle AgedRetrospective StudiesSleep StagesTime Factors

Identifiers

PMID42348802
PMCPMC13313025

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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.