Blind identification of the aortic pressure waveform from multiple peripheral artery pressure waveforms

2007 ◽  
Vol 292 (5) ◽  
pp. H2257-H2264 ◽  
Author(s):  
Gokul Swamy ◽  
Qi Ling ◽  
Tongtong Li ◽  
Ramakrishna Mukkamala
Keyword(s):  
Mean Squared Error ◽  
Systolic Pressure ◽  
Aortic Pressure ◽  
Training Data ◽  
Absolute Pressure ◽  
Pressure Waveform ◽  
Peripheral Artery ◽  
Arterial Tree ◽  
Artery Pressure ◽  
Time Specific

We have developed a new technique to estimate the clinically relevant aortic pressure waveform from multiple, less invasively measured peripheral artery pressure waveforms. The technique is based on multichannel blind system identification in which two or more measured outputs (peripheral artery pressure waveforms) of a single-input, multi-output system (arterial tree) are mathematically analyzed so as to reconstruct the common unobserved input (aortic pressure waveform) to within an arbitrary scale factor. The technique then invokes Poiseuille's law to calibrate the reconstructed waveform to absolute pressure. Consequently, in contrast to previous related efforts, the technique does not utilize a generalized transfer function or any training data and is therefore entirely patient and time specific. To demonstrate proof of concept, we have evaluated the technique with respect to four swine in which peripheral artery pressure waveforms from the femoral and radial arteries and a reference aortic pressure waveform from the descending thoracic aorta were simultaneously measured during diverse hemodynamic interventions. We report that the technique reliably estimated the entire aortic pressure waveform with an overall root mean squared error (RMSE) of 4.6 mmHg. For comparison, the average overall RMSE between the peripheral artery pressure and reference aortic pressure waveforms was 8.6 mmHg. Thus the technique reduced the RMSE by 47%. As a result, the technique also provided similar improvements in the estimation of systolic pressure, pulse pressure, and the ejection interval. With further successful testing, the technique may ultimately be employed for more precise monitoring and titration of therapy in, for example, critically ill and hypertension patients.

An adaptive transfer function for deriving the aortic pressure waveform from a peripheral artery pressure waveform

2009 ◽  
Vol 297 (5) ◽  
pp. H1956-H1963 ◽  
Author(s):  
Gokul Swamy ◽  
Da Xu ◽  
N. Bari Olivier ◽  
Ramakrishna Mukkamala
Keyword(s):  
Transfer Function ◽  
Transfer Functions ◽  
Systolic Pressure ◽  
Aortic Pressure ◽  
Function Technique ◽  
Pressure Waveform ◽  
Peripheral Artery ◽  
Arterial Tree ◽  
Artery Pressure ◽  
Mean Square Errors

We developed a new technique to mathematically transform a peripheral artery pressure (PAP) waveform distorted by wave reflections into the physiologically more relevant aortic pressure (AP) waveform. First, a transfer function relating PAP to AP is defined in terms of the unknown parameters of a parallel tube model of pressure and flow in the arterial tree. The parameters are then estimated from the measured PAP waveform along with a one-time measurement of the wave propagation delay time between the aorta and peripheral artery measurement site (which may be accomplished noninvasively) by exploiting preknowledge of aortic flow. Finally, the transfer function with its estimated parameters is applied to the measured waveform so as to derive the AP waveform. Thus, in contrast to the conventional generalized transfer function, the transfer function is able to adapt to the intersubject and temporal variability of the arterial tree. To demonstrate the feasibility of this adaptive transfer function technique, we performed experiments in 6 healthy dogs in which PAP and reference AP waveforms were simultaneously recorded during 12 different hemodynamic interventions. The AP waveforms derived by the technique showed agreement with the measured AP waveforms (overall total waveform, systolic pressure, and pulse pressure root mean square errors of 3.7, 4.3, and 3.4 mmHg, respectively) statistically superior to the unprocessed PAP waveforms (corresponding errors of 8.6, 17.1, and 20.3 mmHg) and the AP waveforms derived by two previously proposed transfer functions developed with a subset of the same canine data (corresponding errors of, on average, 5.0, 6.3, and 6.7 mmHg).

Wave reflection augments central systolic and pulse pressures during facial cooling

2008 ◽  
Vol 294 (6) ◽  
pp. H2535-H2539 ◽  
Author(s):  
David G. Edwards ◽  
Matthew S. Roy ◽  
Raju Y. Prasad
Keyword(s):  
Cardiovascular Events ◽  
Wave Reflection ◽  
Augmentation Index ◽  
Systolic Pressure ◽  
Aortic Pressure ◽  
Applanation Tonometry ◽  
Pressure Waveform ◽  
Central Aortic Pressure ◽  
Facial Cooling ◽  
Change In Mean

Cardiovascular events are more common in the winter months, possibly because of hemodynamic alterations in response to cold exposure. The purpose of this study was to determine the effect of acute facial cooling on central aortic pressure, arterial stiffness, and wave reflection. Twelve healthy subjects (age 23 ± 3 yr; 6 men, 6 women) underwent supine measurements of carotid-femoral pulse wave velocity (PWV), brachial artery blood pressure, and central aortic pressure (via the synthesis of a central aortic pressure waveform by radial artery applanation tonometry and generalized transfer function) during a control trial (supine rest) and a facial cooling trial (0°C gel pack). Aortic augmentation index (AI), an index of wave reflection, was calculated from the aortic pressure waveform. Measurements were made at baseline, 2 min, and 7 min during each trial. Facial cooling increased ( P < 0.05) peripheral and central diastolic and systolic pressures. Central systolic pressure increased more than peripheral systolic pressure (22 ± 3 vs. 15 ± 2 mmHg; P < 0.05), resulting in decreased pulse pressure amplification ratio. Facial cooling resulted in a robust increase in AI and a modest increase in PWV (AI: −1.4 ± 3.8 vs. 21.2 ± 3.0 and 19.9 ± 3.6%; PWV: 5.6 ± 0.2 vs. 6.5 ± 0.3 and 6.2 ± 0.2 m/s; P < 0.05). Change in mean arterial pressure but not PWV predicted the change in AI, suggesting that facial cooling may increase AI independent of aortic PWV. Facial cooling and the resulting peripheral vasoconstriction are associated with an increase in wave reflection and augmentation of central systolic pressure, potentially explaining ischemia and cardiovascular events in the cold.

Toward a noninvasive subject-specific estimation of abdominal aortic pressure

2008 ◽  
Vol 295 (3) ◽  
pp. H1156-H1164 ◽  
Author(s):  
Carl-Johan Thore ◽  
Jonas Stålhand ◽  
Matts Karlsson
Keyword(s):  
Aortic Pressure ◽  
Processing Technique ◽  
Distributed Model ◽  
Specific Information ◽  
Peripheral Artery ◽  
Tree Model ◽  
Signal Processing Technique ◽  
Arterial Tree ◽  
One Dimensional ◽  
Abdominal Aortic

A method for estimation of central arterial pressure based on linear one-dimensional wave propagation theory is presented in this paper. The equations are applied to a distributed model of the arterial tree, truncated by three-element windkessels. To reflect individual differences in the properties of the arterial trees, we pose a minimization problem from which individual parameters are identified. The idea is to take a measured waveform in a peripheral artery and use it as input to the model. The model subsequently predicts the corresponding waveform in another peripheral artery in which a measurement has also been made, and the arterial tree model is then calibrated in such a way that the computed waveform matches its measured counterpart. For the purpose of validation, invasively recorded abdominal aortic, brachial, and femoral pressures in nine healthy subjects are used. The results show that the proposed method estimates the abdominal aortic pressure wave with good accuracy. The root mean square error (RMSE) of the estimated waveforms was 1.61 ± 0.73 mmHg, whereas the errors in systolic and pulse pressure were 2.32 ± 1.74 and 3.73 ± 2.04 mmHg, respectively. These results are compared with another recently proposed method based on a signal processing technique, and it is shown that our method yields a significantly ( P < 0.01) lower RMSE. With more extensive validation, the method may eventually be used in clinical practice to provide detailed, almost individual, specific information as a valuable basis for decision making.

scholarly journals Noninvasive calculation of the aortic blood pressure waveform from the flow velocity waveform: a proof of concept

2015 ◽  
Vol 309 (5) ◽  
pp. H969-H976 ◽  
Author(s):  
Samuel Vennin ◽  
Alexia Mayer ◽  
Ye Li ◽  
Henry Fok ◽  
Brian Clapp ◽  
...  
Keyword(s):  
Experimental Data ◽  
Mean Arterial Pressure ◽  
Arterial Pressure ◽  
Flow Velocity ◽  
Aortic Pressure ◽  
Pressure Waveform ◽  
Arterial Tree ◽  
Measured Flow ◽  
Rms Error ◽  
Measured Pressure

Estimation of aortic and left ventricular (LV) pressure usually requires measurements that are difficult to acquire during the imaging required to obtain concurrent LV dimensions essential for determination of LV mechanical properties. We describe a novel method for deriving aortic pressure from the aortic flow velocity. The target pressure waveform is divided into an early systolic upstroke, determined by the water hammer equation, and a diastolic decay equal to that in the peripheral arterial tree, interposed by a late systolic portion described by a second-order polynomial constrained by conditions of continuity and conservation of mean arterial pressure. Pulse wave velocity (PWV, which can be obtained through imaging), mean arterial pressure, diastolic pressure, and diastolic decay are required inputs for the algorithm. The algorithm was tested using 1) pressure data derived theoretically from prespecified flow waveforms and properties of the arterial tree using a single-tube 1-D model of the arterial tree, and 2) experimental data acquired from a pressure/Doppler flow velocity transducer placed in the ascending aorta in 18 patients (mean ± SD: age 63 ± 11 yr, aortic BP 136 ± 23/73 ± 13 mmHg) at the time of cardiac catheterization. For experimental data, PWV was calculated from measured pressures/flows, and mean and diastolic pressures and diastolic decay were taken from measured pressure (i.e., were assumed to be known). Pressure reconstructed from measured flow agreed well with theoretical pressure: mean ± SD root mean square (RMS) error 0.7 ± 0.1 mmHg. Similarly, for experimental data, pressure reconstructed from measured flow agreed well with measured pressure (mean RMS error 2.4 ± 1.0 mmHg). First systolic shoulder and systolic peak pressures were also accurately rendered (mean ± SD difference 1.4 ± 2.0 mmHg for peak systolic pressure). This is the first noninvasive derivation of aortic pressure based on fluid dynamics (flow and wave speed) in the aorta itself.

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