Small-computer procedure for optimal filtering of haemodynamic data.

Abstract

Most reported methods dealing with the optimal filtering of haemodynamic data are based on computationally onerous procedures and pay little attention to execution speed. In the paper the problem of filtering noisy aortic flow and pressure waveforms is considered and the possibility of applying a Kalman filter technique, which does not require a preliminary identification of the signal generation process, is discussed. The basic hypothesis is that, in a cardiac cycle, the waveforms considered can be described by functions which permit Taylor series expansion. Thus the signal model is easy to obtain and the filtering procedure is fast, even if implemented on a minicomputer. The filter was applied to flow and pressure signals simultaneously measured in the canine ascending aorta under different physiological conditions. The filter performance is described and discussed.