Aortic Pressure Waveforms Reconstruction Using Simplified Kalman Filter

Abstract

Aortic pressure (Pa) waveforms are important for diagnosis of cardiovascular disease. However, the direct measurement of Pa is invasive and expensive. In the paper, a new simplified Kalman filter (SKF) algorithm for blind system identification was employed for the reconstruction of Pa waveforms using two peripheral artery pressure waveforms. The data of Pa waveforms are collected from 24 human subjects. Simultaneously, brachial artery and femoral artery pressure waveforms data are generated from the simulation of a known two-channel finite impulse response system. In order to study the performance of the proposed SKF algorithm, different amounts of signal-to-noise ratio of the output signal were used in the experiment. Experimental results demonstrated that the proposed SKF algorithm had advantages in comparison with the canonical correlation analysis (CCA) algorithm. It is notable that the proposed SKF algorithm works much more noise-robust than the CCA algorithm in a wide range of SNR.