Modeling carotid and radial artery pulse pressure waveforms by curve fitting with Gaussian functions

Modeling arterial pressure waveforms holds the potential for identifying physiological changes. There is a clinical need for a simple waveform analysis method with a high accuracy in reproducing the original waveforms. The aim of this study was to determine the accuracy of modeling carotid and radial pulses using Gaussian functions, making no physiological assumptions. Carotid and radial pulses were recorded from 20 normal volunteers. Ten consecutive beats from each volunteer were analyzed to determine beat-to-beat variability. Each pulse was decomposed using seven combinations of up to three Gaussian functions. The first function was always positive, but the second or third could be either positive or negative. Three positive Gaussian functions reproduced the original waveforms best with a mean absolute error (MAE) of 1.2% and 1.3% for the carotid and radial pulses respectively, and a maximum residual error of only 4.1% for both. This model had significantly smaller errors than any of the other six (all P < 0.001). Four positive Gaussian functions were then used to test the stability of this model. An insignificant change of the mean MAE (1.2% for both carotid and radial pulses) was obtained, showing that the stability has been reached with three positive Gaussian functions. The variability of MAE calculated as the standard deviation (SD) over the 10 beats was small at 0.2% for both pulses confirming the repeatability of using three positive Gaussian functions.