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 gas-infusion 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.