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

Hanguang Xiao; Lin Qi; Lisheng Xu; Decai Li; Bo Hu; Pengdong Zhao; Huijiao Ren; Jinfeng Huang

doi:10.1016/j.cmpb.2019.105064

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

Comput Methods Programs Biomed. 2019 Dec:182:105064. doi: 10.1016/j.cmpb.2019.105064. Epub 2019 Sep 3.

Authors

Hanguang Xiao¹, Lin Qi², Lisheng Xu², Decai Li³, Bo Hu³, Pengdong Zhao⁴, Huijiao Ren⁴, Jinfeng Huang⁴

Affiliations

¹ College of Artificial Intelligent, Chongqing University of Technology, No. 69 Hongguang Rd, Banan, Chongqing 400050, PR China. Electronic address: hgxiao@cqut.edu.cn.
² Sino-Dutch Biomedical and Information Engineering School, Northeastern University, Shenyang, LiaoNing 110167, PR China.
³ Sichuan Mianyang 404 Hospital, No. 56 Yuejing Road, Fucheng District, Mianyang, Sichuan 400050, PR China.
⁴ College of Artificial Intelligent, Chongqing University of Technology, No. 69 Hongguang Rd, Banan, Chongqing 400050, PR China.

PMID: 31518768
DOI: 10.1016/j.cmpb.2019.105064

Abstract

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_ANN) and reflection index (RI_ANN) of wave reflection in aorta. RM and RI also were estimated by the WSA with a triangle waveform of aortic flow (RM_WSA and RI_WSA) and with a real aortic flow waveform (RM_Ref and RI_Ref) as reference values.

Results: The results showed the correlation coefficient and mean difference between RM_ANN and RM_Ref (R² = 0.92, mean ± standard deviation (SD) = 0.0 ± 0.02) and those between RI_ANN and RI_Ref (R² = 0.91, mean ± SD = 0.0 ± 0.01) were better than those between RM_WSA and RM_Ref (R² = 0.51, mean ± SD = 0.01 ± 0.07) and those between RI_WSA and RI_Ref (R² = 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_ANN VS. RM_Ref and RI_ANN VS. RI_Ref: R² = 0.88 and 0.88, mean ± SD = 0.0 ± 0.05 and 0.0 ± 0.05; RM_WSA VS. RM_Ref and RI_WSA VS. RI_Ref: R² = 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.

Keywords: Artificial neural network; Reflection index; Reflection magnitude; Wave reflection; Wave separation analysis.

MeSH terms

Aorta / physiopathology*
Blood Pressure Determination / methods
Humans
Neural Networks, Computer*
Pulse Wave Analysis*