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Record W2168924718 · doi:10.1109/imtc.2010.5488160

Improvement of oscillometric blood pressure estimates through suppression of breathing effects

2010· article· en· W2168924718 on OpenAlexafffund
S. Chen, Miodrag Bolić, V.Z. Groza, Hilmi R. Dajani, Izmail Batkin, Sreeraman Rajan

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMedicine
TopicECG Monitoring and Analysis
Canadian institutionsDefence Research and Development CanadaUniversity of Ottawa
FundersOntario Centres of Excellence
KeywordsBreathingSIGNAL (programming language)WaveformMultiplicative noiseComputer scienceNoise (video)Blood pressureAdaptive filterControl theory (sociology)Electronic engineeringMedicineAnesthesiaAnalog signalArtificial intelligenceEngineeringAlgorithmTelecommunicationsSignal transfer functionInternal medicine

Abstract

fetched live from OpenAlex

This paper addresses the suppression of the effects of the breathing signal from short duration oscillometric waveform (OMW) recordings to obtain improved blood pressure estimates. As the amplitude modulating effects due to the breathing signal are multiplicative in nature, homomorphic filtering is done on the OMW. An adaptive filtering methodology is adopted to suppress the breathing signal from the OMW. For suppressing the breathing signal, an adaptive noise canceller (ANC) scheme is used when simultaneously acquired reference electrocardiogram (ECG) signal is available while an adaptive line enhancer (ALE) scheme is used when such a reference signal is not readily available. Existing algorithms are used for estimating the blood pressure values. After the suppression of the breathing effects, an improvement in the pressure estimates is observed. Unlike the current methodologies for suppressing the breathing effects from blood pressure measurements, the ALE scheme used in this paper does not require an additional reference signal.

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 score0.064
Threshold uncertainty score0.265

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 head0.007
GPT teacher head0.276
Teacher spread0.269 · 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

Citations7
Published2010
Admission routes2
Has abstractyes

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