In the next stage of the study, we will incorporate a comparator algorithm, further investigate “venous recirculation” and ventilatory inhomogeneity, and ensure that the complete equilibrium of nitrous oxide is established for data collection. Estimated values of VD using the mean and linear regression
approaches are shown in Table 2. Using only CO2, the mean approach produces more consistent estimates of VD than regression at all forcing sinusoidal periods T. By contrast, when using only N2O, estimates of VD using regression are more stable than those obtained using the mean. The reason for such behaviour is demonstrated in Fig. 3(d), selleck where the (x, y) pairs in (30) for CO2 form a dense cluster, while the (x, y) pairs for N2O resemble a straight line. Fig. 4(a) shows that the differences in VA estimates obtained from the tidal and continuous ventilation
models have a mean difference of approximately selleck kinase inhibitor zero, and differences about this mean are not correlated with the mean of the estimates. While differences in the estimates of Q˙P obtained from both models are similarly uncorrelated to the means of the estimates, Fig. 4(b) shows that the mean difference is approximately −0.35 L/min; i.e., the estimate obtained from the continuous model is an average of 0.35 L/min lower than that obtained from the tidal model. Table 3 shows the results of using each model for estimating V D, V A and Q˙P. As described earlier, the tidal ventilation model takes an approach whereby the data acquired
in a session are divided into a set of 20 windows, with an estimate of lung variables provided for each window. The table reports the mean and standard deviation of this set of 20 estimates for the tidal ventilation model, for each session. The continuous ventilation model, however, uses all of the data from a session to produce a single estimate of each lung variable; therefore, the table reports only these single estimates (i.e., without standard deviation) for the continuous ventilation model. The continuous ventilation model uses only the amplitude of indicator gas concentration, without incorporating other variables, hence the underlying physiological information may not be sufficiently characterised. In comparison, a tidal Baricitinib ventilation model allows the examination of the effect of VD, VA, respiratory rates, etc. ( Hahn and Farmery, 2003); therefore variations in variables can be more accurately investigated. The proposed tidal ventilation model is able in theory, with noise-free data, to estimate lung variables using two successive breaths. In practice, it is desirable to use a few more than two breaths for robust estimation for on-line patient monitoring. This procedure is much faster than using the traditional continuous ventilation model, which requires a relatively long data collection time (at least two forcing periods).