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Record W1627791142

Suppression of motion artifacts in optical action potential records by independent component analysis

2012· article· en· W1627791142 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

VenueComputing in Cardiology · 2012
Typearticle
Languageen
FieldMedicine
TopicCardiac electrophysiology and arrhythmias
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsIndependent component analysisArtifact (error)Computer scienceComponent (thermodynamics)Signal processingMotion (physics)Artificial intelligenceMotion analysisSIGNAL (programming language)Pattern recognition (psychology)Blind signal separationComputer visionTelecommunicationsPhysics
DOInot available

Abstract

fetched live from OpenAlex

Optical signals reflect electrical changes in the heart; however, the presence of motion artifact (MA) complicates the evaluation. Possibility of MA suppression by independent component analysis (ICA) method is presented in this article with an analysis of ICA efficiency and its limitations. Suppression of MA by ICA method was compared with results obtained by state-of-the-art signal processing method, the ratio method. Based on this comparison, the ICA was found as highly precise and useful method for motion artifact removal. ICA seems to be a promising tool for analysis of optical signals recorded from the heart surface.

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)0.001
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.216
Threshold uncertainty score0.436

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.017
GPT teacher head0.293
Teacher spread0.276 · 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