Aortic Pressure Waveform Reconstruction Using Simplified Kalman Filter

Wenyan Liu, Zongpeng Li, Yang Yao, Shuran Zhou, Lisheng XU
Northeastern University


Aortic pressure (Pa) waveform is 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 to reconstruct Pa waveform using two peripheral arterial pressure waveforms. The Pa waveform data is collected from 24 human subjects invasively. Simultaneously, brachial and femoral pressure waveform data is generated from the simulation of a known two-channel finite impulse response system. In order to study the performance of the proposed SKF algorithm, the simulated output signals with different signal-to-noise ratios were used in the experiment. Experimental results demonstrated that the proposed SKF algorithm had advantages in comparison with canonical correlation analysis algorithm in different SNRs. We conclude that the proposed SKF algorithm is able to reconstruct Pa waveform.