Evidence map›Paper›PMID 23647099›Full record

ArticleEpilepsia2013

Potential for unreliable interpretation of EEG recorded with microelectrodes.

William C Stacey, Spencer Kellis, Bradley Greger, Christopher R Butson, Paras R Patel, Trevor Assaf, Temenuzhka Mihaylova, Simon Glynn

Abstract read
In one paragraph

Article in Epilepsia, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.

0numbers the graph read from it
0cells of the map it votes in
15citing 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

15 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Universal automated high frequency oscillation detector for real-time, long term EEG.Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology · 2016
    Article
  9. Article
  10. Conundrums of high-frequency oscillations (80-800 Hz) in the epileptic brain.Journal of clinical neurophysiology : official publication of the American Electroencephalographic Society · 2015
    Review
  11. Electrophysiological biomarkers of epilepsy.Neurotherapeutics : the journal of the American Society for Experimental NeuroTherapeutics · 2014
    Review
  12. Article
  13. Article
  14. Article
  15. Article
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

8 authors.

William C StaceyDepartment of Neurology, University of Michigan, Ann Arbor, Michigan 48109-5036, USA. william.stacey@umich.edu
Spencer Kellis
Bradley Greger
Christopher R Butson
Paras R Patel
Trevor Assaf
Temenuzhka Mihaylova
Simon Glynn

Funding

Modeling seizure precursors and antiepileptic brain stimulationK08NS069783 · NINDS · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI STACEY, WILLIAM CHARLES · 2011 to 2015
$886k
NINDS NIH HHS K08 NS069783NINDS NIH HHS K08NS069783
6 · The paper itself

Abstract

purposeRecent studies in epilepsy, cognition, and brain machine interfaces have shown the utility of recording intracranial electroencephalography (iEEG) with greater spatial resolution. Many of these studies utilize microelectrodes connected to specialized amplifiers that are optimized for such recordings. We recently measured the impedances of several commercial microelectrodes and demonstrated that they will distort iEEG signals if connected to clinical EEG amplifiers commonly used in most centers. In this study we demonstrate the clinical implications of this effect and identify some of the potential difficulties in using microelectrodes.

methodsHuman iEEG data were digitally filtered to simulate the signal recorded by a hybrid grid (two macroelectrodes and eight microelectrodes) connected to a standard EEG amplifier. The filtered iEEG data were read by three trained epileptologists, and high frequency oscillations (HFOs) were detected with a well-known algorithm. The filtering method was verified experimentally by recording an injected EEG signal in a saline bath with the same physical acquisition system used to generate the model. Several electrodes underwent scanning electron microscopy (SEM). KEY

findingsMacroelectrode recordings were unaltered compared to the source iEEG signal, but microelectrodes attenuated low frequencies. The attenuated signals were difficult to interpret: all three clinicians changed their clinical scoring of slowing and seizures when presented with the same data recorded on different sized electrodes. The HFO detection algorithm was oversensitive with microelectrodes, classifying many more HFOs than when the same data were recorded with macroelectrodes. In addition, during experimental recordings the microelectrodes produced much greater noise as well as large baseline fluctuations, creating sharply contoured transients, and superimposed "false" HFOs. SEM of these microelectrodes demonstrated marked variability in exposed electrode surface area, lead fractures, and sharp edges. SIGNIFICANCE: Microelectrodes should not be used with low impedance (<1 GΩ) amplifiers due to severe signal attenuation and variability that changes clinical interpretations. The current method of preparing microelectrodes can leave sharp edges and nonuniform amounts of exposed wire. Even when recorded with higher impedance amplifiers, microelectrode data are highly prone to artifacts that are difficult to interpret. Great care must be taken when analyzing iEEG from high impedance microelectrodes.

Indexed as

Brain MappingMicroelectrodesAlgorithmsArtifactsBrainBrain WavesDatabases, FactualElectric ImpedanceElectroencephalographyElectronic Data ProcessingEpilepsyHumansElectrocorticographyElectrodesHigh frequency oscillationsImpedance

Identifiers

PMID23647099
PMCPMC3731390

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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.