基于体位特征使用混杂模型预测血压对于无支撑后背的反应

Kaur Gurmanik; Arora Shatru; Jain Kumar

doi:10.1631/FITEE.1400295

Your Location：

Home >

Browse articles >

基于体位特征使用混杂模型预测血压对于无支撑后背的反应

计算机与自动化 | Updated：2022-06-06

- 基于体位特征使用混杂模型预测血压对于无支撑后背的反应
- Using hybrid models to predict blood pressure reactivity to unsupported back based on anthropometric characteristics
- 信息与电子工程前沿（英文） 2015年16卷第6期页码：474-485
- Affiliations：
  
  Sant Longowal Institute of Engineering and Technology, Deemed University, Punjab 148106, India
- Author bio：
  
  ‡Corresponding Author
  †E-mail:mannsliet@gmail.comajatsliet@yahoo.comvkjain27@yahoo.com
- Funds：
- DOI：10.1631/FITEE.1400295
  中图分类号：
- 收稿：2014-08-18，
  
  修回：2015-05-07，
  
  录用：2014-12-20，
  
  纸质出版：2015-03
- Accepted：
Scan QR Code
基于体位特征使用混杂模型预测血压对于无支撑后背的反应[J]. 信息与电子工程前沿（英文）, 2015,16(6):474-485.

Kaur Gurmanik, Arora Shatru, Jain Kumar. Using hybrid models to predict blood pressure reactivity to unsupported back based on anthropometric characteristics[J]. Frontiers of Information Technology & Electronic Engineering, 2015, 16(6): 474-485.
基于体位特征使用混杂模型预测血压对于无支撑后背的反应[J]. 信息与电子工程前沿（英文）, 2015,16(6):474-485. DOI： 10.1631/FITEE.1400295.

Kaur Gurmanik, Arora Shatru, Jain Kumar. Using hybrid models to predict blood pressure reactivity to unsupported back based on anthropometric characteristics[J]. Frontiers of Information Technology & Electronic Engineering, 2015, 16(6): 474-485. DOI： 10.1631/FITEE.1400295.

摘要

目的

准确测量血压（BP）对于流行病学研究、筛查规划、调研研究和高血压相关病变（冠心病、中风、肾衰竭等）的早期诊断及预防有重要意义。被测者体位对于准确测量血压有重要影响。血压测量指南建议测试时被测者应在后背有支撑的情况下保持坐姿，以避免血压读数偏高。本文使用混杂模型预测血压对于无支撑后背的反应。

创新点

本文考虑血压正常和高血压测试者的人体预测变量（如年龄、身高、体重、体块指数和上臂周长(AC)），使用基于PCA的前向逐步回归（PCA-SWR）、基于PCA的人工神经网络（PCA-ANN）、基于PCA的自适应神经模糊推理系统（PCA-ANFIS）和基于PCA的最小方差支持向量机（PCA-LS-SVM）等模型预测血压对无支撑后背的反应。

方法

使用PCA消除人体预测变量间的多重共线性，并在原始数据集中选取主元（PC）。所选主元被输入至所建立预测模型用于建模及测试。

结论

通过评估合适的统计指标（确定性系数、平均平方根误差、平均绝对百分比误差），得出较之其他模型，PCA-LS-SVM对于预测血压反应较有前景。此评估也展示了混杂模型在预测生物医学领域其他参数时的重要性和先进性。

Abstract

Accurate blood pressure (BP) measurement is essential in epidemiological studies

screening programmes

and research studies as well as in clinical practice for the early detection and prevention of high BP-related risks such as coronary heart disease

stroke

and kidney failure. Posture of the participant plays a vital role in accurate measurement of BP. Guidelines on measurement of BP contain recommendations on the position of the back of the participants by advising that they should sit with supported back to avoid spuriously high readings. In this work

principal component analysis (PCA) is fused with forward stepwise regression (SWR)

artificial neural network (ANN)

adaptive neuro-fuzzy inference system (ANFIS)

and the least squares support vector machine (LS-SVM) model for the prediction of BP reactivity to an unsupported back in normotensive and hypertensive participants. PCA is used to remove multi-collinearity among anthropometric predictor variables and to select a subset of components

termed ‘principal components’ (PCs)

from the original dataset. The selected PCs are fed into the proposed models for modeling and testing. The evaluation of the performance of the constructed models

using appropriate statistical indices

shows clearly that a PCA-based LS-SVM (PCA-LS-SVM) model is a promising approach for the prediction of BP reactivity in comparison to others. This assessment demonstrates the importance and advantages posed by hybrid models for the prediction of variables in biomedical research studies.

关键词

Keywords

references

R Baba , M Koketsu , M Nagashima , 等 . Adolescent obesity adversely affects blood pressure and resting heart rate . Circ J , 2007 . 71 ( 5 ): 722 - 726 . DOI: 10.1253/circj.71.722 http://doi.org/10.1253/circj.71.722 .

K Barbé , Y Kurylyak , F Lamonaca . Logistic ordinal regression for the calibration of oscillometric blood pressure monitors . Biomed Signal Process , 2014 . 11 89 - 96 . DOI: 10.1016/j.bspc.2014.01.012 http://doi.org/10.1016/j.bspc.2014.01.012 .

IA Basheer , M Hajmeer . Artificial neural networks: fundamentals, computing, design, and application . J Microbiol Methods , 2000 . 43 ( 1 ): 3 - 31 . DOI: 10.1016/S0167-7012(00)00201-3 http://doi.org/10.1016/S0167-7012(00)00201-3 .

British Hypertension Society Blood Pressure Measurement (CD-ROM) . 1998 . BMJ Books, London, UK . .

DH Card , DL Peterson , PA Matson . Prediction of leaf chemistry by the use of visible and near infrared reflectance spectroscopy . Remote Sens Environ , 1988 . 26 ( 2 ): 123 - 147 . DOI: 10.1016/0034-4257(88)90092-2 http://doi.org/10.1016/0034-4257(88)90092-2 .

C Chiatti , S Bustacchini , G Furneri , 等 . The economic burden of inappropriate drug prescribing, lack of adherence and compliance, adverse drug events in older people . Drug Saf , 2012 . 35 ( 1 ): 73 - 87 . DOI: 10.1007/BF03319105 http://doi.org/10.1007/BF03319105 .

WC Cushman , KM Cooper , RA Horne , 等 . Effect of back support and stethoscope head on seated blood pressure determinations . Am J Hypertens , 1990 . 3 ( 3 ): 240 - 241 . .

M de Hoog , M van Eijsden , K Stronks , 等 . Association between body size and blood pressure in children from different ethnic origins . Cardiovasc Diabetol , 2012 . 11 136.1 - 13610 . DOI: 10.1186/1475-2840-11-136 http://doi.org/10.1186/1475-2840-11-136 .

M Forouzanfar , HR Dajani , VZ Groza , 等 . Feature-based neural network approach for oscillometric blood pressure estimation . IEEE Trans Instrum Meas , 2011 . 60 ( 8 ): 2786 - 2796 . DOI: 10.1109/TIM.2011.2123210 http://doi.org/10.1109/TIM.2011.2123210 .

S Genc . Prediction of mean arterial blood pressure with linear stochastic models . 2011 . Proc. IEEE Annual Int. Conf. on Engineering in Medicine and Biology Society . 712 - 715 . DOI: 10.1109/IEMBS.2011.6090161 http://doi.org/10.1109/IEMBS.2011.6090161 .

HF Golino , LSB Amaral , SFP Duarte , 等 . Predicting increased blood pressure using machine learning . J Obes , 2014 . 2014 637635.1 - 63763512 . DOI: 10.1155/2014/637635 http://doi.org/10.1155/2014/637635 .

DN Gujarati . Basic Econometrics , 1995 . McGraw-Hill, New York, USA , .

MT Hagan , MB Menhaj . Training feedforward networks with the Marquardt algorithm . IEEE Trans Neur Netw , 1994 . 5 ( 6 ): 989 - 993 . DOI: 10.1109/72.329697 http://doi.org/10.1109/72.329697 .

RB Haynes , DL Sackett , DW Taylor , 等 . Increased absenteeism from work after detection and labeling of hypertensive patients . New Engl J Med , 1978 . 299 ( 14 ): 741 - 744 . DOI: 10.1056/NEJM197810052991403 http://doi.org/10.1056/NEJM197810052991403 .

HH Huang , T Xu , J Yang . Comparing logistic regression, support vector machines, and permanental classification methods in predicting hypertension . 2014 . BMC Proc . ( Suppl. 1 ): S96.1 - S965 . DOI: 10.1186/1753-6561-8-S1-S96 http://doi.org/10.1186/1753-6561-8-S1-S96 .

M Inoue , M Minami , E Yano . Body mass index, blood pressure, and glucose and lipid metabolism among permanent and fixed-term workers in the manufacturing industry: a cross-sectional study . BMC Pub Health , 2014 . 14 207.1 - 2078 . DOI: 10.1186/1471-2458-14-207 http://doi.org/10.1186/1471-2458-14-207 .

JSR Jang . ANFIS: adaptive-network-based fuzzy inference system . IEEE Trans Syst Man Cybern , 1993 . 23 ( 3 ): 665 - 685 . DOI: 10.1109/21.256541 http://doi.org/10.1109/21.256541 .

IT Jolliffe . Principal Component Analysis , 2002 . Springer-Verlag, New York, USA , .

HF Kaiser . The application of electronic computers to factor analysis . Educ Psychol Meas , 1960 . 20 141 - 151 . DOI: 10.1177/001316446002000116 http://doi.org/10.1177/001316446002000116 .

SMU Khan , JS Manzoor , SUJ Lee . Predicting student blood pressure by support vector machine using Facebook . 2014 . Proc. IEEE World Congress on Services . 486 - 492 . DOI: 10.1109/SERVICES.2014.92 http://doi.org/10.1109/SERVICES.2014.92 .

OO Kolade , TM O’Moore-Sullivan , M Stowasser , 等 . Arterial stiffness, central blood pressure and body size in health and disease . Int J Obes , 2012 . 36 ( 1 ): 93 - 99 . DOI: 10.1038/ijo.2011.79 http://doi.org/10.1038/ijo.2011.79 .

Y Kurylyak , F Lamonaca , D Grimaldi . A neural network-based method for continuous blood pressure estimation from a PPG signal . 2013 . Proc. IEEE Int. Conf. on Instrumentation and Measurement Technology . 280 - 283 . DOI: 10.1109/I2MTC.2013.6555424 http://doi.org/10.1109/I2MTC.2013.6555424 .

S Lynn , J Ringwood , E Ragnoli , 等 . Virtual metrology for plasma etch using tool variables . 2009 . Proc. IEEE/SEMI Advanced Semiconductor Manufacturing Conf . 143 - 148 . DOI: 10.1109/ASMC.2009.5155972 http://doi.org/10.1109/ASMC.2009.5155972 .

E Monte-Moreno . Non-invasive estimate of blood glucose and blood pressure from a photoplethysmograph by means of machine learning techniques . Artif Intell Med , 2011 . 53 ( 2 ): 127 - 138 . DOI: 10.1016/j.artmed.2011.05.001 http://doi.org/10.1016/j.artmed.2011.05.001 .

DC Moser , ICB Giuliano , ACK Titski , 等 . Anthropometric measures and blood pressure in school children . J Pediatr , 2013 . 89 ( 3 ): 243 - 249 . DOI: 10.1016/j.jped.2012.11.006 http://doi.org/10.1016/j.jped.2012.11.006 .

D Nauck . Neuro-Fuzzy Systems , 1997 . John Wiley & Sons, Inc., New York, USA , .

R Noori , A Khakpour , B Omidvar , 等 . Comparison of ANN and principal component analysis— multivariate linear regression models for predicting the river flow based on developed discrepancy ratio statistics . Expert Syst Appl , 2010 . 37 ( 8 ): 5856 - 5862 . DOI: 10.1016/j.cswa.2010.02.020 http://doi.org/10.1016/j.cswa.2010.02.020 .

E O’Brien , R Asmar , L Beilin , 等 . European society of hypertension recommendations for conventional, ambulatory and home blood pressure measurement . J Hypertens , 2003 . 21 ( 5 ): 821 - 848 . .

TG Pickering , JE Hall , LJ Appel , 等 . Recommendations for blood pressure measurement in humans and experimental animals. Part 1: Blood pressure measurement in humans: a statement for professionals from the Subcommittee of Professional and Public Education of the American Heart Association Council on High Blood Pressure Research . Hypertension , 2005 . 45 142 - 161 . DOI: 10.1161/01.HYP.0000150859.47929.8e http://doi.org/10.1161/01.HYP.0000150859.47929.8e .

B Schölkopf , AJ Smola . Learning with Kernels , 2002 . MIT Press, Cambridge, MA, USA , .

S Sharma . Applied Multivariate Techniques , 1995 . John Wiley & Sons, Inc., Canada , .

KW Smith , MS Sasaki . Decreasing multicollinearity: a method for models with multiplicative functions . Sociol Methods Res , 1979 . 8 ( 1 ): 35 - 56 . DOI: 10.1177/004912417900800102 http://doi.org/10.1177/004912417900800102 .

JAK Suykens , J Vandewalle . Least squares support vector machine classifiers . Neur Process Lett , 1999 . 9 ( 3 ): 293 - 300 . DOI: 10.1023/A:1018628609742 http://doi.org/10.1023/A:1018628609742 .

S Tobias , JE Carlson . Brief report: Bartlett’s test of sphericity and chance findings in factor analysis . Multivar Behav Res , 1969 . 4 ( 3 ): 375 - 377 . DOI: 10.1207/s15327906mbr0403_8 http://doi.org/10.1207/s15327906mbr0403_8 .

B Williams , T Brown , A Onsman . Exploratory factor analysis: a five-step guide for novices . Aust J Paramed , 2010 . 8 ( 3 ): 1.1 - 113 . DOI: 10.1023/A:1011131521933 http://doi.org/10.1023/A:1011131521933 .

浏览量

Downloads

CSCD

文章被引用时，请邮件提醒。

Submit

工具集

关联资源

Port and radiation pattern decoupled metasurface-loaded patch antenna using deep-learning-assisted optimization for MIMO applications