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Model-based oscillometric blood pressure estimation

2014· article· en· 8 citations· W1996056068 on OpenAlex· 10.1109/memea.2014.6860103

Why is this work in the frame?

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

Canadian affiliationAn author listed a Canadian institution. This is the only route the usual frame has.
Canadian funderA Canadian agency funded it. The work may carry no Canadian affiliation at all.

The three-model screen

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All three models called this out of scope.

stratum: aff_core · design weight: 5595.24 (the sample is stratified; any rate computed without the weight is wrong)
Claude Opus 4.8OUT
genre: empirical
about Canada: no
confidence: high

Biomedical engineering paper proposing a model-based algorithm for oscillometric blood pressure estimation; the object is a device measurement method.

GPT-5.6 (high)OUT
genre: conceptual
about Canada: no
confidence: high

It reviews biomedical blood-pressure estimation algorithms, not research methodology.

Grok 4.5OUT
genre: empirical
about Canada: no
confidence: high

Biomedical engineering of oscillometric blood-pressure algorithms; clinical measurement, not research practice.

Abstract

Oscillometry is the most common measurement method used in automated electronic blood pressure (BP) monitors. A variety of oscillometric BP algorithms exist in the literature. However, most of these algorithms are without physiological and theoretical foundation. Moreover, most of the existing oscillometric algorithms estimate the BP from the envelope of the oscillometric pulses and ignore the wealth of information that the oscillometric pulses contain. More information could be obtained from the amplitude and time characteristics of the oscillometric pulses at different cuff pressures if an accurate mathematical model is developed. This paper reviews three novel model-based oscillometric BP estimation methods developed by our research group. These methods include (i) mathematical modeling of the oscillometric waveform envelope and BP estimation using neural networks, (ii) mathematical modeling of the oscillometric waveform and parameter estimation using extended Kalman filter, and (iii) mathematical modeling of the pulse transit time (PTT) and estimation of BP based on PTT-cuff pressure dependency. The performance of the proposed methods was evaluated on simulated and actual data in terms of mean error, mean absolute error, and standard deviation of error.

Stored with the screening record, where it is evidence for the labels above.

The record

Venue
Topic
Non-Invasive Vital Sign Monitoring
Field
Engineering
Canadian institutions
University of Ottawa
Funders
Natural Sciences and Engineering Research Council of CanadaMinistero dello Sviluppo EconomicoOntario Centres of Excellence
Keywords
WaveformKalman filterEnvelope (radar)Computer scienceMean squared errorApproximation errorAlgorithmMathematicsArtificial intelligenceStatistics
Has abstract in OpenAlex
yes