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Record W2151512267 · doi:10.1109/ccece.2004.1347686

Multifractal characterization of synthetic ECG in the presence of coloured noise

2004· article· en· W2151512267 on OpenAlexaff
Murray Potter, Witold Kinsner

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMedicine
TopicECG Monitoring and Analysis
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsMultifractal systemNoise (video)AttractorWhite noiseFractal dimensionMathematicsNoise measurementFractalComputer sciencePattern recognition (psychology)AlgorithmArtificial intelligenceStatistical physicsStatisticsPhysicsNoise reductionMathematical analysis

Abstract

fetched live from OpenAlex

The electrocardiogram (ECG) is a signal resulting from a complicated dynamical process. The paper describes an extension to a previous method of reconstruction and characterization of real ECG to a new "realistic" dynamical model of an ECG. A noise-free ECG is generated at 360 samples per second from the dynamical model, and contaminated with 10 dB of white, pink, or brown noise. The dynamical phase-space is then reconstructed. It is observed that the noise-free ECG has a Renyi fractal dimension spectrum (RS) bounded between 1 and 2. It is also observed that noise introduces a positive bias in the RS estimate. This bias is greater for uncorrelated noise, but more asymmetric with increasing correlation. These are the first characterizations of a realistic estimate of the noise-free ECG attractor. It is also the first analysis of the effect of noise correlation on the RS of ECGs in isolation of sampling effects. This demonstrates that a disagreement with the RS of a real ECG signal is not due to noise.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.410
Threshold uncertainty score0.094

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 head0.012
GPT teacher head0.264
Teacher spread0.252 · 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

Citations2
Published2004
Admission routes1
Has abstractyes

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