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 algorithm to compute O₂ consumption and CO₂ production by adopting a stochastic deconvolution method, which controls relative weight of fidelity to the data and smoothness of the estimates. Performance of the new algorithm was compared with those by other algorithms (moving average, trends identification, Kalman filter and Kalman smoothing) against validation tests, in which energy metabolism was evaluated at every 1 min. First, in silico simulation study, rectangular or sinusoidal inputs of gradually decreasing period (64, 32, 16 and 8 min) were applied, and samples collected from the output were corrupted with superimposed noise. Secondly, CO₂ was infused into a chamber in gradually decreasing intervals, and CO₂ production rate was estimated by algorithms. In terms of recovery, mean square error and correlation to known input signal in the validation tests, deconvolution performed better than the other algorithms. Finally, as a case study, time course of energy metabolism during sleep, stages of which were assessed by a standard polysomnogram, was measured in a whole body indirect calorimeter. Analysis of covariance revealed association of energy expenditure with sleep stage, and energy expenditure computed by deconvolution and Kalman smoothing was more closely associated with sleep stages than those based on trends identification and Kalman filter. The new algorithm significantly improved transient response of the whole body indirect calorimeter.