Application of the general linear model for smoothing gas exchange data

The precision of an interpretation of gas exchange records in progressive exercise is limited by the typical breath-to-breath variation in the data. Recently, two procedures have been proposed for minimizing the "noise" in the estimates of alveolar gas exchange time series data. One approach utilizes an estimate of pulmonary blood flow (Q) for smoothing purposes. The other approach utilizes an estimate of effective lung volume (V'L) for smoothing purposes. In this paper, we formulate the smoothing problem as a general linear model and demonstrate the concurrent estimates of both V'L and Q. Furthermore, we investigate the interaction between V'L and Q. Specifically, when a high value of lung volume is used (such as the subject's resting functional residual capacity) in the alveolar gas exchange algorithm, the estimate of Q is biased low and the result is a less effective smoothing of the data. In addition, we demonstrate how the Q estimate can be improved by utilizing more appropriate estimates of arterial carbon dioxide tension.