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Record W2169765232 · doi:10.1109/iembs.2008.4649361

Real-time automated neural-network sleep classifier using single channel EEG recording for detection of narcolepsy episodes

2008· article· en· W2169765232 on OpenAlexaff
S. R. I. Gabran, S. Zhang, M.M.A. Salama, R.R. Mansour, Cheryl L. George

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldNeuroscience
TopicEEG and Brain-Computer Interfaces
Canadian institutionsWestern UniversityLondon Health Sciences CentreUniversity of Waterloo
Fundersnot available
KeywordsComputer scienceElectroencephalographyNarcolepsyArtificial intelligenceArtificial neural networkSleep StagesClassifier (UML)Sleep (system call)Pattern recognition (psychology)Speech recognitionPolysomnographyNeurosciencePsychologyNeurology

Abstract

fetched live from OpenAlex

Conventional sleep staging and classification methods involve complicated settings to acquire multiple electrophysiological signals for extended recording durations, followed by specialists' analysis which is a time consuming exercise. These procedures need to be carried out in sleep clinics and are not suitable for applications based on real-time sleep monitoring and analysis. In this paper, a real-time sleep staging and classification technique is proposed using single EEG channel based on an artificial neural network classifier. This method is optimized to run on portable processing platforms with limited processing capabilities.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.541
Threshold uncertainty score0.712

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.064
GPT teacher head0.282
Teacher spread0.218 · 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 teacher head, not a consensus.

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

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

Citations12
Published2008
Admission routes1
Has abstractyes

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