title: Blind identification of the aortic pressure waveform from multiple peripheral artery pressure waveforms.
abstract: 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.
authors.affiliation: Department of Electrical and Computer Engineering, Michigan State University, East Lansing, MI 48824, USA.
authors.forename: Gokul
authors.initials: G
authors.lastname: Swamy
authors.forename: Qi
authors.initials: Q
authors.lastname: Ling
authors.forename: Tongtong
authors.initials: T
authors.lastname: Li
authors.forename: Ramakrishna
authors.initials: R
authors.lastname: Mukkamala
mesh_terms: D000465:Algorithms; D000818:Animals; D001011:Aorta / Q000502:physiology*; D001794:Blood Pressure / Q000502:physiology*; D001795:Blood Pressure Determination / Q000379:methods*; D003198:Computer Simulation; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D005263:Femoral Artery / Q000502:physiology*; D008955:Models, Cardiovascular; D017534:Radial Artery / Q000502:physiology*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013552:Swine