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 (VO
₂
) and carbon dioxide production (VCO
₂
). However, the relatively large room size required for patient comfort creates low signal-to-noise ratio for the VO
₂
and VCO
₂
signals. We proposed a wavelet-based approach to efficiently remove noise while retaining important dynamic changes in the VO
₂
and VCO
₂
. We used correlated noise modeled from gas-infusion experiments superimposed on theoretical VO
₂
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 VO
₂
, 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.