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Record W2513431239 · doi:10.1109/eit.2016.7535279

Ballistocardiogram signal as a measure of cardio-postural variation during orthostatic challenge

2016· article· en· W2513431239 on OpenAlexafffund
Amanmeet Garg, Da Xu, Michelle Bruner, Andrew P. Blaber, Kouhyar Tavakolian

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicNon-Invasive Vital Sign Monitoring
Canadian institutionsSimon Fraser University
FundersMitacs
KeywordsQUIETSIGNAL (programming language)Orthostatic vital signsCoherence (philosophical gambling strategy)Center of pressure (fluid mechanics)Blood pressurePhysical medicine and rehabilitationBallistocardiographyComputer scienceMedicineMathematicsPhysicsCardiologyStatisticsInternal medicine

Abstract

fetched live from OpenAlex

Ballistocardiogram (BCG) signal is created by the movement of the blood in the body, and has been recorded via standing on a force platform. Prolonged quiet stance is well understood to induce postural sway. Previous work has shown that blood pressure (BP), posture muscle activation (EMG) and center of pressure (COP) signals are related during quiet stance. In this study, we investigated the relationship between the BCG and BP, EMG and COP signal pairs to address the central question whether the BCG signal is associated with cardio-postural interactions during orthostatic challenge. The wavelet transform coherence method was applied to obtain time-frequency varying estimate of the coherence between the signal pairs. The significant (>threshold) and time-varying coherence found in the three frequency ranges (0.1-0.5, 0.05-0.1, & 0.01- 0.5Hz) suggested the presence of a time-frequency dependent relationship between the signal pairs. The results from this study highlight a potential interaction of the BCG signal with other relevant physiological signals during orthostatic challenge.

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 score0.282
Threshold uncertainty score0.608

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 head0.009
GPT teacher head0.200
Teacher spread0.191 · 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

Citations1
Published2016
Admission routes2
Has abstractyes

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