Optimizing energy expenditure detection in human metabolic chambers.

Whole-room indirect calorimeters are capable of measuring human metabolic rate in conditions representative of quasi-free-living state through measurement of oxygen consumption (VO2) and carbon dioxide production (VCO2). However, the relatively large room size required for patient comfort creates low signal-to-noise ratio for the VO2 and VCO2 signals. We proposed a wavelet-based approach to efficiently remove noise while retaining important dynamic changes in the VO2 and VCO2. We used correlated noise modeled from gasinfusion experiments superimposed on theoretical VO2 sequences to test the accuracy of a wavelet based processing method. The wavelet filtering is demonstrated to improve the accuracy and sensitivity of minute-to-minute changes in VO2, while maintaining stability during steady-state periods. The wavelet method is shown to have a lower mean absolute error and reduced total error when compared to standard methods of processing calorimeter signals.