Estimation of cardiac output from peripheral pressure waveforms using Laguerre model blind system identification

We have developed a new technique, which may enable a more accurate, complete characterization of the circulatory system, including local or global hydrodynamic phenomena, using multiple measurements from several anatomic locations and/or multiple modalities. This technique, Laguerre model blind system identification (LMBSI), uses a Laguerre function series expansion to provide a compact but complete quantitative description of the distinct behavior of two or more circulatory waveforms. LMBSI identifies a set of five parameters per channel plus one common parameter that can be treated as a feature vector and used to predict cardiovascular parameters of interest. Standard statistical techniques can be used to extract information from that compact feature vector. In this paper, multiparameter regression is used to predict cardiac output, using two separate arterial pressure waveforms and the LMBSI algorithm. This serves as a proof-of-principle that two distinct circulatory waveforms, with LMBSI, can be used to characterize the circulatory system. In the future, this technique might be applied to noninvasive circulatory measurements.