Bernard Olivier, Alata Olivier, Francaux Marc
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.
J Appl Physiol (1985). 2006 Mar;100(3):1049-58. doi: 10.1152/japplphysiol.00712.2005. Epub 2005 Oct 27.
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.
在时域中,使用经验模型对高强度运动的非稳态氧气摄取动力学进行建模时,通常采用基于梯度下降的方法。然而,当建模函数不可连续微分以及估计对应不适定问题时,这些程序可能会削弱参数估计的可信度。为了解决这些问题,将模拟退火(SA)方法的一种实现与GRG2算法(一种以其稳健性闻名的梯度下降方法)进行了比较。生成了40个模拟的摄氧量响应,以模拟从低强度到高强度运动转变的实际时间过程,信噪比等于20分贝。使用这两种估计方法,用一个不连续的双指数函数对它们进行了两次建模。与SA相比,GRG2显著使第一个指数的两个估计动力学参数(时间延迟td1和时间常数tau1)产生偏差,并损害了基线A0、td1和tau1的精度(即标准差)。SA显著提高了第二个指数的三个参数(渐近增量A2、时间延迟td2和时间常数tau2)的精度。然而,td2在两种程序中均显著产生偏差,并且整个第二个成分参数的大置信区间限制了它们的解释。为了在实验数据上比较这两种算法,26名受试者在功率自行车上每人进行了两次从80瓦到最大摄氧量80%的转变,并逐次测量摄氧量。使用SA算法进行的动力学参数估计中,超过88%在实验数据点和模型之间产生了最低的残差平方和。对于A1,GRG2的重复性系数更好,而对于A2和tau2,SA的更好。我们的结果表明,SA的实现显著改善了大多数这些动力学参数的估计,但在估计第二个指数的参数值时仍存在较大误差。