Optimizing energy expenditure detection in human metabolic chambers

Abstract

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.

Fig. 1.

The derivative of a simulated room O₂ concentration, d(fO₂)/dt, corrupted by colored noise. The Central Difference method of calculating the d(fO₂)/dt (red line) captures sharp changes, but contains significant noise during the steady-state period (>150 min). Applying the wavelet transform to the central difference (green line) reduces the noise while retaining sharp features.

Fig. 2.

Methods of calculating VO₂ from noisy fO₂ data. The Henning Method (blue line, top row), Central Difference Method (CDM, red line, middle row), and Central Difference Method with Wavelet De-noising (green line, bottom row) were tested with various theoretical VO₂ sequences, such as short (1 and 2 minute) pulses (left column) and longer (10-30 minute) pulses (right column).