A whole body indirect calorimeter provides accurate measurement of energy expenditure over long periods of time, but it has limitations to assess its dynamic changes. The present study aimed to improve algorithms to compute O(2) consumption and CO(2) production by adopting a stochastic deconvolution method, which controls the relative weight of fidelity to the data and smoothness of the estimates. The performance of the new algorithm was compared with that of other algorithms (moving average, trends identification, Kalman filter, and Kalman smoothing) against validation tests in which energy metabolism was evaluated every 1 min. First, an in silico simulation study, rectangular or sinusoidal inputs of gradually decreasing periods (64, 32, 16, and 8 min) were applied, and samples collected from the output were corrupted with superimposed noise. Second, CO(2) was infused into a chamber in gradually decreasing intervals and the CO(2) production rate was estimated by algorithms. In terms of recovery, mean square error, and correlation to the known input signal in the validation tests, deconvolution performed better than the other algorithms. Finally, as a case study, the time course of energy metabolism during sleep, the stages of which were assessed by a standard polysomnogram, was measured in a whole body indirect calorimeter. Analysis of covariance revealed an association of energy expenditure with sleep stage, and energy expenditure computed by deconvolution and Kalman smoothing was more closely associated with sleep stages than that based on trends identification and the Kalman filter. The new algorithm significantly improved the transient response of the whole body indirect calorimeter.