Estimation of wave reflection in aorta from radial pulse waveform by artificial neural network: a numerical study.

Background And Objective: Wave reflection in aorta has been shown to have incremental value for predicting cardiovascular events. However, its estimation by wave separation analysis (WSA) is complex.

Methods: In this study, a novel method was proposed based on a cascade artificial neural network (ANN) for wave reflection estimation by the frequency features of radial pressure waveform alone. The simulation database of 4000 samples was generated by a 55-segment transmission line model of human arterial tree and was used for evaluating the ANN with 10-fold cross validation for the estimation of reflection magnitude (RM) and reflection index (RI) of wave reflection in aorta. RM and RI also were estimated by the WSA with a triangle waveform of aortic flow (RM and RI) and with a real aortic flow waveform (RM and RI) as reference values.

Results: The results showed the correlation coefficient and mean difference between RM and RM (R = 0.92, mean ± standard deviation (SD) = 0.0 ± 0.02) and those between RI and RI (R = 0.91, mean ± SD = 0.0 ± 0.01) were better than those between RM and RM (R = 0.51, mean ± SD = 0.01 ± 0.07) and those between RI and RI (R = 0.50, mean ± SD = 0.0 ± 0.02). As the sample diversity in the simulation database was increased but the total number of samples keeps constant, the advantage of the ANN, though decreased slightly, became more significant than those of WSA (RM VS. RM and RI VS. RI: R = 0.88 and 0.88, mean ± SD = 0.0 ± 0.05 and 0.0 ± 0.05; RM VS. RM and RI VS. RI: R = 0.24 and 0.24, mean ± SD = 0.07 ± 0.24 and 0.02 ± 0.08, respectively). In addition, the ANN can achieve better results than the traditional method WSA even only two hidden neurons are used.

Conclusions: ANN is a potential method for the estimation of wave reflection in aorta by a single radial pulse waveform, but further validation of this method in clinic trials is needed.