Parametric model derivation of transfer function for noninvasive estimation of aortic pressure by radial tonometry

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Abstract
Aortic pressure can be estimated noninvasively by applying a transfer function (TF) to radial tonometry signals. This study compares the performance of prior approaches, based on Fourier transform and inverted aortic-to-radial model, with direct radial-to-aortic autoregressive exogenous (ARX) model. Simultaneous invasive aortic pressure and radial tonometry pressure were recorded during rest in 39 patients in the supine position. Individual radial-aortic TF's were estimated from 20 patients, and the average TF was used to predict aortic pressures in the remaining 19 patients. The direct average TF yielded accurate aortic systolic pressure estimation (error 0.4/spl plusmn/2.9 mmHg) and good reproduction of the aortic pressure waveform (root mean squared error 2.2/spl plusmn/0.9 mmHg). The inverted reverse TF (aortic radial) yielded comparable results, while the Fourier-based TF had worse performance. Individual direct TF provided improved predictive accuracy only for indexes which are based on higher frequency components of the waveform (augmentation index, systolic time period). An ARX average TF can be used to accurately estimate central aortic pressure waveform parameters from noninvasive radial pulse tracings, and its performance is superior to previous techniques.