Evidence map›Paper›PMID 29685173›Full record

ArticleBiomedical engineering online2018

Dynamic characteristics of oxygen consumption.

Lin Ye, Ahmadreza Argha, Hairong Yu, Branko G Celler, Hung T Nguyen, Steven Su

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Article in Biomedical engineering online, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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1citing papers in PubMed
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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Lin YeSchool of Biomedical Engineering, University of Technology Sydney, 15 Broadway, Sydney, Australia.
Ahmadreza ArghaSchool of Electrical Engineering, University of New South Wales, Sydney, Australia.
Hairong YuSchool of Biomedical Engineering, University of Technology Sydney, 15 Broadway, Sydney, Australia.
Branko G CellerSchool of Electrical Engineering, University of New South Wales, Sydney, Australia.
Hung T NguyenSchool of Biomedical Engineering, University of Technology Sydney, 15 Broadway, Sydney, Australia.
Steven SuSchool of Biomedical Engineering, University of Technology Sydney, 15 Broadway, Sydney, Australia. steven.su@uts.edu.au.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundPrevious studies have indicated that oxygen uptake ([Formula: see text]) is one of the most accurate indices for assessing the cardiorespiratory response to exercise. In most existing studies, the response of [Formula: see text] is often roughly modelled as a first-order system due to the inadequate stimulation and low signal to noise ratio. To overcome this difficulty, this paper proposes a novel nonparametric kernel-based method for the dynamic modelling of [Formula: see text] response to provide a more robust estimation.

methodsTwenty healthy non-athlete participants conducted treadmill exercises with monotonous stimulation (e.g., single step function as input). During the exercise, [Formula: see text] was measured and recorded by a popular portable gas analyser ([Formula: see text], COSMED). Based on the recorded data, a kernel-based estimation method was proposed to perform the nonparametric modelling of [Formula: see text]. For the proposed method, a properly selected kernel can represent the prior modelling information to reduce the dependence of comprehensive stimulations. Furthermore, due to the special elastic net formed by [Formula: see text] norm and kernelised [Formula: see text] norm, the estimations are smooth and concise. Additionally, the finite impulse response based nonparametric model which estimated by the proposed method can optimally select the order and fit better in terms of goodness-of-fit comparing to classical methods.

resultsSeveral kernels were introduced for the kernel-based [Formula: see text] modelling method. The results clearly indicated that the stable spline (SS) kernel has the best performance for [Formula: see text] modelling. Particularly, based on the experimental data from 20 participants, the estimated response from the proposed method with SS kernel was significantly better than the results from the benchmark method [i.e., prediction error method (PEM)] ([Formula: see text] vs [Formula: see text]).

conclusionsThe proposed nonparametric modelling method is an effective method for the estimation of the impulse response of VO

Indexed as

Models, BiologicalOxygen ConsumptionAthletesExerciseHumansMaleCardiorespiratory response to treadmill exerciseDynamical modellingImpulse response identificationKernel methodOxygen uptake

Identifiers

PMID29685173
PMCPMC5914074

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LicenceCC BY
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.