Identification of multi-channel cardiovascular system using dual-pole Laguerre basis functions for assessment of aortic flow and TPR

This paper presents a novel method to identify the multichannel cardiovascular system using two distinct peripheral blood pressure signals. The method can characterize the distinct arterial path dynamics that shape each of the blood pressure signals, and recover the common aortic flow signal fed to them. A Laguerre series data compression technique is used to obtain a compact representation of the cardiovascular system, whose coefficients are identified using the multi-channel blind system identification. A Laguerre model de-convolution algorithm is developed to stably recover the aortic flow signal. Persistent excitation, model identifiability, and asymptotic variance are analyzed to quantify the method's validity and reliability. From the identified Laguerre series coefficients of the cardiovascular dynamics, mean aortic flow and total peripheral resistance are estimated. Experimental results based on 7,000 data segments obtained from 9 swine models show that the waveform of the aortic flow is stably recovered from peripheral blood pressure signals and that the cardiovascular dynamics can be identified very reliably for all the swine models under diverse physiologic conditions. In addition, the use of the identified cardiovascular dynamics results in the improvement in estimating the mean aortic flow and total peripheral resistance by 60% and 45% in terms of the R-2 value, compared to their standard counterparts.