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

BACKGROUND AND OBJECTIVES: High-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. METHODS: We 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). RESULTS: Fifty-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.

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Journal
Neurology
Published
2026-06-25
DOI
https://doi.org/10.1212/wnl.0000000000218225
Primary Topic
Epilepsy research and treatment
Type
article
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article

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

S. Gliske, Zhuying Chen, William C. Stacey, Kevin Tyner et al.
Neurology
Epilepsy research and treatment
article

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

S. Gliske, Zhuying Chen, William C. Stacey, Kevin Tyner, Abdallah Alsammani, Srijita Das, Jack Lin, David B. Grayden, Garnett Hegeman, Garnett Smith, Moustafa Mutahr
article en

Abstract

BACKGROUND AND OBJECTIVES: High-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. METHODS: We 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). RESULTS: Fifty-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.

NeurologyVol. 107(2)
Delaware State University (US), The University of Melbourne (AU), University of Michigan (US), BioSurfaces (United States) (US), University of Nebraska Medical Center (US)
Openalex Percentile: Top 7%
Epilepsy research and treatment
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