Determination of aortic pulse transit time based on waveform decomposition of radial pressure wave.

Carotid-femoral pulse transit time (cfPTT) is a widely accepted measure of central arterial stiffness. The cfPTT is commonly calculated from two synchronized pressure waves. However, measurement of synchronized pressure waves is technically challenging. In this paper, a method of decomposing the radial pressure wave is proposed for estimating cfPTT. From the radial pressure wave alone, the pressure wave can be decomposed into forward and backward waves by fitting a double triangular flow wave. The first zero point of the second derivative of the radial pressure wave and the peak of the dicrotic segment of radial pressure wave are used as the peaks of the fitted double triangular flow wave. The correlation coefficient between the measured wave and the estimated forward and backward waves based on the decomposition of the radial pressure wave was 0.98 and 0.75, respectively. Then from the backward wave, cfPTT can be estimated. Because it has been verified that the time lag estimation based on of backward wave has strong correlation with the measured cfPTT. The corresponding regression function between the time lag estimation of backward wave and measured cfPTT is y = 0.96x + 5.50 (r = 0.77; p < 0.001). The estimated cfPTT using radial pressure wave decomposition based on the proposed double triangular flow wave is more accurate and convenient than the decomposition of the aortic pressure wave based on the triangular flow wave. The significance of this study is that arterial stiffness can be directly estimated from a noninvasively measured radial pressure wave.

Estimation of aortic pulse wave velocity based on waveform decomposition of central aortic pressure waveform.

Objective.Aortic stiffness is associated with risk of cardiovascular events. Carotid-femoral pulse wave velocity (cfPWV) is the current noninvasive gold standard for assessing aortic stiffness. However, the cfPWV measurement is challenging, requiring simultaneous signals at the carotid and femoral sites.Approach.In this study, the aortic PWV is estimated using a single radial pressure waveform and compared with cfPWV. 111 subjects' aortic PWVs are estimated from the decomposition of the derived central aortic pressure waveform based on three types of reconstructed flow waveform: the peak of triangular flow waveform based on 30% ejection time (Q30%tri), the peak of triangular flow waveform based on inflection point (Qtri), and averaged flow waveform (Qavg). The central aortic pressure waveform is derived from a radial pressure waveform via a validated transfer function.Main results.TheQavgis used for estimating aortic PWV without the determination of the peak point of the triangular flow waveforms. The estimated aortic PWV shows good agreement with cfPWV. The mean difference ± SD is 0.29 ± 1.50 m s-1(r2 = 0.29,p< 0.001) for theQ30%tri; 0.27 ± 1.40 m s-1(r2 = 0.38,p < 0.001) for theQtri; 0.23 ± 1.39 m s-1(r2 = 0.40,p < 0.001) for theQavg. The correlation between estimated aortic PWV based onQ30%triand measured cfPWV is weak. The results ofQtriandQavgshow no obvious difference.Significance.The proposed method can be used as a less complex way than conventional measurement of cfPWV to further assess arterial stiffness and predict cardiovascular risks or events.

Aortic pressure waveform reconstruction using a multi-channel Newton blind system identification algorithm.

BACKGROUND: Central aortic pressure (CAP) as the major load on the left heart is of great importance in the diagnosis of cardiovascular disease. Studies have pointed out that CAP has a higher predictive value for cardiovascular disease than peripheral artery pressure (PAP) measured by means of traditional sphygmomanometry. However, direct measurement of the CAP waveform is invasive and expensive, so there remains a need for a reliable and well validated non-invasive approach. METHODS: In this study, a multi-channel Newton (MCN) blind system identification algorithm was employed to noninvasively reconstruct the CAP waveform from two PAP waveforms. In simulation experiments, CAP waveforms were recorded in a previous study, on 25 patients and the PAP waveforms (radial and femoral artery pressure) were generated by FIR models. To analyse the noise-tolerance of the MCN method, variable amounts of noise were added to the peripheral signals, to give a range of signal-to-noise ratios. In animal experiments, central aortic, brachial and femoral pressure waveforms were simultaneously recorded from 2 Sprague-Dawley rats. The performance of the proposed MCN algorithm was compared with the previously reported cross-relation and canonical correlation analysis methods. RESULTS: The results showed that the root mean square error of the measured and reconstructed CAP waveforms and less noise-sensitive using the MCN algorithm was smaller than those of the cross-relation and canonical correlation analysis approaches. CONCLUSION: The MCN method can be exploited to reconstruct the CAP waveform. Reliable estimation of the CAP waveform from non-invasive measurements may aid in early diagnosis of cardiovascular disease.