Identification of Multichannel Cardiovascular Dynamics Using Dual Laguerre Basis Functions for Noninvasive Cardiovascular Monitoring

This paper presents a novel method to identify the cardiovascular (CV) system using two distinct peripheral blood pressure (BP) signals. The method can characterize the distinct arterial path dynamics that shape each of the BP signals and recover the common central-flow signal fed to them. A Laguerre series data-compression technique is used to obtain a compact representation of the CV system, whose coefficients are identified using the multichannel blind system identification. A Laguerre model deconvolution algorithm is developed to stably recover the central-flow signal. Persistent excitation, model identifiability, and asymptotic variance are analyzed to quantify the method's validity and reliability, without using any direct measurement of central-flow input signal. Experimental results based on 7000 data segments obtained from nine swine subjects show that, for all the swine subjects under diverse physiologic conditions, the CV dynamics can be identified very reliably and the waveform of the central flow can be recovered stably from peripheral BP signals.