Dynamic characteristics of oxygen consumption

• Published in: Biomedical engineering online Vol. 17; no. 1; p. 44
• Main Authors: Ye, Lin, Argha, Ahmadreza, Yu, Hairong, Celler, Branko G, Nguyen, Hung T, Su, Steven
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 23.04.2018; BioMed Central; BMC
• Subjects: Cardiorespiratory response to treadmill exercise; Dynamical modelling; Exercise tests; Impulse response identification; Kernel method; Oxygen transport; Oxygen uptake; Physiological aspects; Australia; Dynamical modelling; Impulse response identification; Oxygen uptake; Cardiorespiratory response to treadmill exercise; Kernel method
• ISSN: 1475-925X; 1475-925X
• DOI: 10.1186/s12938-018-0476-6

Previous 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. Twenty 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. Several 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]). The proposed nonparametric modelling method is an effective method for the estimation of the impulse response of VO -Speed system. Furthermore, the identified average nonparametric model method can dynamically predict [Formula: see text] response with acceptable accuracy during treadmill exercise.