IDENTIFICATION OF CARDIOVASCULAR DYNAMICS FROM PERIPHERAL CIRCULATORY WAVEFORMS USING LAGUERRE MODEL BLIND SYSTEM ID

摘要

A new tool has been developed for characterizing both the global and local dynamic behavior exhibited by peripheral circulatory waveforms. The signals representing these waveforms can be derived from a variety of different cardiovascular sensor modalities measuring changes in pressure, flow, or volume. The correlation that exists between two peripheral circulatory waveform measurements taken at different locations allows this new signal-processing algorithm to identify two compact, low order dynamic models in realtime. These models identify the distinct dynamic behavior of the circulatory paths traveled by the pulsatile waveforms using output measurements alone. This new algorithm is based on a multi-channel blind system identification technique that has been reformulated to use a Laguerre basis function series expansion. Additionally, a new deconvolution algorithm has been developed to allow estimation of the unknown common input using the identified Laguerre models, which are generally inversely unstable. This new approach has been shown to provide accurate identification of vascular hemodynamics when applied to experimental data derived from a swine.