Evidence map›Paper›PMID 38843758›Full record

ArticleClinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology2024

Optimizing automated detection of high frequency oscillations using visual markings does not improve SOZ localization.

Trisha Mendoza, Casey L Trevino, Daniel W Shrey, Jack J Lin, Indranil Sen-Gupta, Beth A Lopour

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In one paragraph

Article in Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Networks through the lens of high-frequency oscillations.Frontiers in network physiology · 2024
    Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Trisha MendozaDepartment of Biomedical Engineering, University of California, Irvine, Irvine, CA, USA.
Casey L TrevinoDepartment of Biomedical Engineering, University of California, Irvine, Irvine, CA, USA.
Daniel W ShreyDivision of Neurology, Children's Hospital of Orange County, Orange, CA, USA; Department of Pediatrics, University of California, Irvine, Orange, CA, USA.
Jack J LinUC Davis Comprehensive Epilepsy Program, Department of Neurology, Davis, CA, USA; UC Davis Center for Mind and Brain, Davis, CA, USA.
Indranil Sen-GuptaComprehensive Epilepsy Program, Department of Neurology, University of California, Irvine, Irvine, CA, USA.
Beth A LopourDepartment of Biomedical Engineering, University of California, Irvine, Irvine, CA, USA. Electronic address: beth.lopour@uci.edu.

Funding

Seizure localization for epilepsy surgery using high frequency electrophysiological markersR01NS116273 · NINDS · UNIVERSITY OF CALIFORNIA-IRVINE · PI LOPOUR, BETH ANN · 2021 to 2025
$2.0M
NINDS NIH HHS R01 NS116273
6 · The paper itself

Abstract

objectiveHigh frequency oscillations (HFOs) are a biomarker of the seizure onset zone (SOZ) and can be visually or automatically detected. In theory, one can optimize an automated algorithm's parameters to maximize SOZ localization accuracy; however, there is no consensus on whether or how this should be done. Therefore, we optimized an automated detector using visually identified HFOs and evaluated the impact on SOZ localization accuracy.

methodsWe detected HFOs in intracranial EEG from 20 patients with refractory epilepsy from two centers using (1) unoptimized automated detection, (2) visual identification, and (3) automated detection optimized to match visually detected HFOs.

resultsSOZ localization accuracy based on HFO rate was not significantly different between the three methods. Across patients, visually optimized detector settings varied, and no single set of settings produced universally accurate SOZ localization. Exploratory analysis suggests that, for many patients, detection settings exist that would improve SOZ localization.

conclusionsSOZ localization accuracy was similar for all three methods, was not improved by visually optimizing detector settings, and may benefit from patient-specific parameter optimization. SIGNIFICANCE: Visual HFO marking is laborious, and optimizing automated detection using visual markings does not improve localization accuracy. New patient-specific detector optimization methods are needed.

Indexed as

Drug Resistant EpilepsyAdolescentAdultAlgorithmsBrain WavesElectrocorticographyElectroencephalographyEpilepsyFemaleHumansMaleMiddle AgedSeizuresYoung AdultAutomatic DetectionHigh Frequency OscillationsParameter OptimizationRefractory EpilepsySeizure Onset Zone LocalizationVisual Detection

Identifiers

PMID38843758
PMCPMC11798584

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Registered trials

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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.