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Record W2920295506

Ischemic stroke detection using EEG signals

2018· article· en· W2920295506 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueComputer Science and Software Engineering · 2018
Typearticle
Languageen
FieldNeuroscience
TopicEEG and Brain-Computer Interfaces
Canadian institutionsMcMaster University
Fundersnot available
KeywordsElectroencephalographyComputer scienceStroke (engine)Gold standard (test)Decision treeWearable computerArtificial intelligenceMultilayer perceptronIschemic strokePattern recognition (psychology)Magnetic resonance imagingArtificial neural networkMedicineCardiologyIschemiaInternal medicineRadiologyEngineering
DOInot available

Abstract

fetched live from OpenAlex

Stroke is the second leading cause of death in the United States of America. 87% of all strokes are ischemic stroke, which is mainly caused by the blockage of small blood vessels around the brain. Magnetic resonance imaging (MRI) provides the gold standard for accurate diagnosis of ischemic strokes, but it is both time-consuming and unsuitable for 24/7 monitoring. In this paper, we propose an ischemic stroke detection method through the multi-domain analysis of EEG brain signal from wearable EEG devices and machine learning. Using 40 healthy and 40 patients' data, we find that Multi-Layered Perceptron (MLP) and Bootstrap models (Extra-Tree and Decision-Tree) can achieve test accuracy of 95% with an area under the ROC curve 0.85.

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.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.442
Threshold uncertainty score0.543

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.022
GPT teacher head0.245
Teacher spread0.223 · 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