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Record W2163414043 · doi:10.1109/tbme.2009.2024265

Wavelet Phase Synchronization Analysis of Cerebral Blood Flow Autoregulation

2010· article· en· W2163414043 on OpenAlexafffund
Tingying Peng, A.B. Rowley, Philip N. Ainslie, Marc J. Poulin, Stephen J. Payne

Bibliographic record

VenueIEEE Transactions on Biomedical Engineering · 2010
Typearticle
Languageen
FieldMedicine
TopicTraumatic Brain Injury and Neurovascular Disturbances
Canadian institutionsUniversity of Calgary
FundersCanadian Institutes of Health Research
KeywordsCerebral autoregulationPhase synchronizationCerebral blood flowSynchronization (alternating current)AutoregulationSpeech recognitionComputer sciencePhysicsMathematicsPhase (matter)Blood pressureMedicineAnesthesiaInternal medicineTopology (electrical circuits)Combinatorics

Abstract

fetched live from OpenAlex

The dynamic relationship between beat-to-beat mean arterial blood pressure (ABP) fluctuations and cerebral blood flow velocity (CBFV) variations have been intensively studied. The experimentally observed low coherence in the low-frequency band has previously indicated that the assumptions of linearity and/or stationarity, the preconditions of the linear transfer function analysis, are not valid in that frequency region. Latka et al. [M. Latka, M. Turalska, M. Glaubic-Latka, W. Kolodziej, D. Latka, and B. J. J. West, "Phase dynamics in cerebral autoregulation," Amer. J. Physiol. Heart Circ. Physiol., vol. 289 pp. H2272-H2279, Jul. 2005] used a wavelet phase synchronization method to identify the instantaneous phase difference between ABP and CBFV, and low values of synchronization index were found in the low-frequency range, seeming to provide further evidence that the cerebral autoregulation system is nonstationary. Here, we focus on another possible factor corresponding for this low synchronization index-unmeasured variability. We demonstrate analytically and with a physiologically based cerebral hemodynamic model that, in the case of multiple inputs, the phase difference between one input, ABP, and the output, CBFV, will be distorted by an additional input, end-tidal CO(2) (P(ETCO(2))), and no longer accurately represent the true ABP-CBFV system phase shift. We also prove that this phase distortion can be corrected if the transfer functions for ABP-CBFV and P(ETCO(2))-CBFV are known or can be estimated. A significantly increased value of synchronization index in the low-frequency band is found by using the CO(2) correction term with experimental data on 13 subjects. This essentially indicates that the lack of synchronization between ABP and CBFV previously identified by Latka et al. [M. Latka, M. Turalska, M. Glaubic-Latka, W. Kolodziej, D. Latka, and B. J. J. West, "Phase dynamics in cerebral autoregulation," Amer. J. Physiol. Heart Circ. Physiol., vol. 289, pp. H2272-H2279, Jul. 2005] can be partly attributed to unmeasured variability.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.810
Threshold uncertainty score0.496

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.008
GPT teacher head0.245
Teacher spread0.237 · 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 designSimulation or modeling
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

Citations53
Published2010
Admission routes2
Has abstractyes

Explore more

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