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Cellular and Molecular Biology, special issue on "Biomedical Signal and Image Processing" 53, 2 (2007) 11-21
Parameter estimation and order selection for an empirical model of VO2 on-kinetics
Olivier Alata 1, Olivier Bernard 2
(01/05/2007)

In humans, VO2 on-kinetics are noisy numerical signals that reflect the pulmonary oxygen exchange kinetics at the onset of exercise. They are empirically modelled as a sum of an offset and delayed exponentials. The number of delayed exponentials, i.e. the order of the model, is commonly supposed to be one for low-intensity exercises and two for high-intensity exercises. As no ground truth has ever been provided to validate these postulates, physiologists still need statistical methods to verify their hypothesis about the number of exponentials of the VO2 on-kinetics especially in the case of high- intensity exercises. Our objectives are first to develop accurate methods for estimating the parameters of the model at a fixed order, and then, to propose statistical tests for selecting the appropriate order. In this paper, we provide, on simulated Data, performances of Simulated Annealing for estimating model parameters and performances of Information Criteria for selecting the order. These simulated Data are generated with both single-exponential and double-exponential models, and noised by white and Gaussian noise. The performances are given at various Signal to Noise Ratio (SNR). Considering parameter estimation, results show that the confidences of estimated parameters are improved by increasing the SNR of the response to be fitted. Considering model selection, results show that Information Criteria are adapted statistical criteria to select the number of exponentials.
1 :  SIGNAL-IMAGE-COMMUNICATION (SIC)
CNRS : FRE2731 – Université de Poitiers
2 :  Laboratoire des Adaptations Physiologiques aux Activités Physiques (LAPHAP)
Université de Poitiers : EA3813
SIC
Informatique/Traitement du signal et de l'image

Sciences de l'ingénieur/Traitement du signal et de l'image
VO2 on-kinetics – parametric modelling – parameter estimation – model selection – optimisation – stochastic methods – general linear model – simulated annealing – information criteria

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