基于子空间法的主动脉压力脉搏波重建

有创测量中心主动脉压力波形(central aortic pressure waveform， CAPW)存在一定的风险且花费较大.通常用外周动脉压力波形(peripheral arterial pressure waveform， PAPW)来代替CAPW，而PAPW与CAPW有差异性.虽然盲系统辨识方法能够实时重建与动态辨识中心主动脉压力波形，但该方法在模型定阶上还需改进，因此本文提出了基于Hankel矩阵的秩定阶结合子空间方法用外周动脉压力波形重建中心主动脉压力波形.临床数据验证算法的结果表明，实测的与估计的CAPW的均方根误差约为5.87mmHg，波形匹配度约为74.70%;动物数据进一步验证算法，结果表明实测的与估计的CAPW的均方根误差约为8.82mmHg，波形匹配度为54.57%.实验验证了该方法的有效性.

Reconstruction of Aortic Pressure Wave Based on Subspace Algorithm

Invasive measurement of central aortic pressure waveform(CAPW)is risky and costly. Peripheral arterial pressure waveform(PAPW)has been used to substitute CAPW. However， there is a difference between PAPW and CAPW. Although the blind system identification technology can reconstruct in real-time and dynamically estimate CAPW， it needs to be improved in optimizing the model ordering. Accordingly， a rank-based ordering method was proposed based on Hankel matrix combining the subspace method to reconstruct the central aortic pressure waveform with two peripheral pressure waveforms. The results of the validation algorithm based on the clinical data showed that the root mean square error of the measured CAPW and the estimated CAPW is about 5.87mmHg， and the waveform fit is about 74.70%. The results of the validation algorithm based on the collecting animal data showed that the root mean square error of the measured CAPW and the estimated CAPW is about 8.82mmHg， and the waveform fit is about 54.57%. Thus the experiments verified the effectiveness of the method.