title: On the modeling of breath-by-breath oxygen uptake kinetics at the onset of high-intensity exercises: simulated annealing vs. GRG2 method.
abstract: Modeling in the time domain, the non-steady-state O2 uptake on-kinetics of high-intensity exercises with empirical models is commonly performed with gradient-descent-based methods. However, these procedures may impair the confidence of the parameter estimation when the modeling functions are not continuously differentiable and when the estimation corresponds to an ill-posed problem. To cope with these problems, an implementation of simulated annealing (SA) methods was compared with the GRG2 algorithm (a gradient-descent method known for its robustness). Forty simulated Vo2 on-responses were generated to mimic the real time course for transitions from light- to high-intensity exercises, with a signal-to-noise ratio equal to 20 dB. They were modeled twice with a discontinuous double-exponential function using both estimation methods. GRG2 significantly biased two estimated kinetic parameters of the first exponential (the time delay td1 and the time constant tau1) and impaired the precision (i.e., standard deviation) of the baseline A0, td1, and tau1 compared with SA. SA significantly improved the precision of the three parameters of the second exponential (the asymptotic increment A2, the time delay td2, and the time constant tau2). Nevertheless, td2 was significantly biased by both procedures, and the large confidence intervals of the whole second component parameters limit their interpretation. To compare both algorithms on experimental data, 26 subjects each performed two transitions from 80 W to 80% maximal O2 uptake on a cycle ergometer and O2 uptake was measured breath by breath. More than 88% of the kinetic parameter estimations done with the SA algorithm produced the lowest residual sum of squares between the experimental data points and the model. Repeatability coefficients were better with GRG2 for A1 although better with SA for A2 and tau2. Our results demonstrate that the implementation of SA improves significantly the estimation of most of these kinetic parameters, but a large inaccuracy remains in estimating the parameter values of the second exponential.
authors.affiliation: Laboratoire des Adaptations Physiologiques aux Activités Physiques, LAPHAP EA 3813 Université de Poitiers, Faculté des Sciences du Sport, 4 allée Jean Monnet, 86000 Poitiers, France. olivier.bernard@univ-poitiers.fr
authors.forename: Olivier
authors.initials: O
authors.lastname: Bernard
authors.forename: Olivier
authors.initials: O
authors.lastname: Alata
authors.forename: Marc
authors.initials: M
authors.lastname: Francaux
mesh_terms: D000465:Algorithms; D001696:Biomechanical Phenomena; D016001:Confidence Intervals; D015444:Exercise / Q000502:physiology*; D006801:Humans; D008433:Mathematics; D008954:Models, Biological; D017711:Nonlinear Dynamics; D010101:Oxygen Consumption; D015203:Reproducibility of Results; D012119:Respiration; D012129:Respiratory Function Tests; D012143:Respiratory Physiological Phenomena