Biomechanics and Blood Pressure with Modeling of Pulse Wave Velocity Based on Multiple Linear Regression

Abstract:

The purpose of present study is to build a multiple linear regression model using biomechanical theory to assess the relationship of pulse wave velocity (PWV) with blood pressure, height and age. By testing the PWV, blood pressure, height, weight of 164 female adults aged above 45 and existing data, the author constructed a multiple linear regression equation. Through comparing the practical test PWV values with the estimate values from regression model, the result showed that there was no significant difference between the model assessment and practical test values (t=0.833, p=.423>.05). Therefore, the regression model is fit for assessing PWV value by height, age, systolic and diastolic pressure.

• Periodical: Advanced Materials Research (Volume 952)
• Pages: 261-264
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.952.261
• Online since: May 2014
• Authors: Long Jia*
• Keywords: Biomechanics, Blood Pressure, Modeling, Multiple Linear Regressions, Pulse Wave Velocity

References

[1] Nimon, Kim F.; Oswald, Frederick L. Understanding the Results of Multiple Linear Regression: Beyond Standardized Regression Coefficients. Organizational Research Methods. Oct2013, Vol. 16 Issue 4, pp.650-674.

DOI: 10.1177/1094428113493929

[2] Ge, Yonghui; Han, Xiaohui. The Extent of Gross Errors Eliminated by Robust Multiple Linear Regressions. Communications in Statistics: Theory & Methods. 2013, Vol. 42 Issue 23, pp.4210-4221.

DOI: 10.1080/03610926.2011.648791

[3] Yamamoto, Yohei; Perron, Pierre. Estimating and testing multiple structural changes in linear models using band spectral regressions. Econometrics Journal. Oct2013, Vol. 16 Issue 3, pp.400-429.

DOI: 10.1111/ectj.12010

[4] Mokhtari, Mohammad H.; Busu, Ibrahim; Mokhtari, Hossein; Zahedi, Gholamreza; Sheikhattar, Leila; Movahed, Mohammad A. Neural Network and Multiple Linear Regression for Estimating Surface Albedo from ASTER Visible and Near-Infrared Spectral Bands. Earth Interactions. 2013, Vol. 17 Issue 3, pp.1-20.

DOI: 10.1175/2011ei000424.1

[5] Salvi, Paolo; Palombo, Carlo; Salvi, Giovanni Matteo; Labat, Carlos; Parati, Gianfranco; Benetos, Athanase, Left ventricular ejection time, not heart rate, is an independent correlate of aortic pulse wave velocity. Journal of Applied Physiology Dec2013, Vol. 115 Issue 6, p.1610.

DOI: 10.1152/japplphysiol.00475.2013

[6] Gao, Mingwu; Zhang, Guanqun; Olivier, N. Bari; Mukkamala, Ramakrishna. Improved Pulse Wave Velocity Estimation Using an Arterial Tube-Load Model. IEEE Transactions on Biomedical Engineering. Mar 2014, Vol. 61 Issue 3, pp.848-858.

DOI: 10.1109/tbme.2013.2291385

[7] Sangle, Shirish R.; Tanikawa, Akiko; Schreiber, Karen; Zakalka, Marina; D'Cruz, David P. The prevalence of abnormal pulse wave velocity, pulse contour analysis and ankle–brachial index in patients with livedo reticularis: a controlled study. Rheumatology. Nov2013, Vol. 52 Issue 11, pp.1992-1998.

DOI: 10.1093/rheumatology/ket227

[8] Shahmirzadi, Danial; Narayanan, Prathyush; Li, Ronny X.; Qaqish, William W.; Konofagou, Elisa E., Mapping the longitudinal wall stiffness heterogeneities within intact canine aortas using Pulse Wave Imaging (PWI) ex vivo. Journal of Biomechanics 2013, Vol. 46 Issue 11, p.1866.

DOI: 10.1016/j.jbiomech.2013.04.019

[9] Laugesen, Esben; Rossen, Niklas B.; Høyem, Pernille et al. Reproducibility of pulse wave analysis and pulse wave velocity in patients with type 2 diabetes. Scandinavian Journal of Clinical & Laboratory Investigation. Aug2013, Vol. 73 Issue 5, pp.428-435.

DOI: 10.3109/00365513.2013.800578