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New Chronic EEG Electrode for Critical/Intensive Care Unit Monitoring

2005· article· en· W2048040179 on OpenAlexaff
John R. Ives

Bibliographic record

VenueJournal of Clinical Neurophysiology · 2005
Typearticle
Languageen
FieldNeuroscience
TopicEEG and Brain-Computer Interfaces
Canadian institutionsLondon Health Sciences Centre
Fundersnot available
KeywordsElectrodeElectroencephalographyLimitingBiomedical engineeringComputer scienceMaterials scienceMedicineChemistryEngineeringMechanical engineeringPsychiatry

Abstract

fetched live from OpenAlex

The main limiting factor of EEG monitoring in the critical/intensive care environments is, and always has been, the recording electrode. The electrode and its application to the scalp has changed very little since EEG was first discovered and developed as a clinical tool. However, the evolution of amplifiers and data acquisition systems have made tremendous strides. Modern-day EEG recording systems now have the capability to record for days and weeks with little intervention, whereas the EEG electrode requires constant attention and skilled adjustment every 10 to 24 hours. If one surveys the vast array of electrodes used now and in the past, the only electrode that, once placed, never needed any further adjustment for days and weeks on end, was the chronic silver-silver/chloride (Ag-Ag/Cl) sphenoidal (Sp) electrode. This Sp electrode has now been modified to permit it to be placed subdermally, similar to that of a subdermal needle electrode, but now the needle is removed to leave in place a fine, flexible, durable, chronic Ag-Ag/Cl electrode. Once placed, this subdermal wire electrode (SWE, patent pending) starts to record immediately with a low impedance of 3 to 4 Komega. This electrode can record any biopotential, in humans and in animals, and in most recording environments; it never needs adjustment, and records high-quality biopotential signals for as long as it is left in place. The SWE is also MRI and computed-tomography compatible. It takes less than half the time to place the SWE, and placement can now be performed by any medically trained personnel to obtain a low-maintenance, high-quality EEG recording.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.007
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.003

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.104
GPT teacher head0.435
Teacher spread0.331 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreMethods

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations70
Published2005
Admission routes1
Has abstractyes

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