Literature DB >> 28690044

A new mathematical model of wrist pulse waveforms characterizes patients with cardiovascular disease - A pilot study.

Dianning He1, Lu Wang2, Xiaobing Fan3, Yang Yao4, Ning Geng5, Yingxian Sun5, Lisheng Xu6, Wei Qian4.   

Abstract

The purpose of this study was to analyze and compare a series of measured radial pulse waves as a function of contact pressure for young and old healthy volunteers, and old patients with cardiovascular disease. The radial pulse waves were detected with a pressure sensor and the contact pressure of the sensor was incremented by 20gf during the signal acquisition. A mathematical model of radial pulse waveform was developed by using two Gaussian functions modulated by radical functions and used to fit the pulse waveforms. Then, a ratio of area (rA) and a ratio of peak height (rPH) between percussion wave and dicrotic wave as a function of contact pressure were calculated based on fitted parameters. The results demonstrated that there was a maximum for waveform peak height, a minimum for rA (rAmin) and a minimum for rPH (rPHmin) appeared as contact pressure varied. On average, older patients had higher peak amplitude and a significantly smaller rAmin (p<0.001) and rPHmin (p<0.02) than the young and old volunteers. The rAmin and rPHmin calculated with the mathematical model had moderate to strong positive linear correlations (r=0.66 to 0.84, p<0.006) with those directly calculated without the model. The receiver operating characteristic (ROC) analysis showed that the rAmin calculated with the model and the contact pressure measured at the rAmin had good diagnostic accuracy to distinguish healthy volunteers vs. diseased patients. Therefore, using the mathematical model to quantitatively analyze the radial pulse waveforms as a function of contact pressure could be useful in the diagnosis of cardiovascular diseases.
Copyright © 2017 IPEM. Published by Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  Cardiovascular disease; Contact pressure; Gaussian function; Mathematical model; Radial pulse waves

Mesh:

Year:  2017        PMID: 28690044     DOI: 10.1016/j.medengphy.2017.06.022

Source DB:  PubMed          Journal:  Med Eng Phys        ISSN: 1350-4533            Impact factor:   2.242


  5 in total

1.  A Wearable and Real-Time Pulse Wave Monitoring System Based on a Flexible Compound Sensor.

Authors:  Xiaoxiao Kang; Jun Zhang; Zheming Shao; Guotai Wang; Xingguang Geng; Yitao Zhang; Haiying Zhang
Journal:  Biosensors (Basel)       Date:  2022-02-20

2.  Quantitative Comparison of the Performance of Piezoresistive, Piezoelectric, Acceleration, and Optical Pulse Wave Sensors.

Authors:  Hongju Wang; Lu Wang; Nannan Sun; Yang Yao; Liling Hao; Lisheng Xu; Stephen E Greenwald
Journal:  Front Physiol       Date:  2020-01-14       Impact factor: 4.566

3.  Training Convolutional Neural Networks on Simulated Photoplethysmography Data: Application to Bradycardia and Tachycardia Detection.

Authors:  Andrius Sološenko; Birutė Paliakaitė; Vaidotas Marozas; Leif Sörnmo
Journal:  Front Physiol       Date:  2022-07-18       Impact factor: 4.755

4.  A 3D Wrist Pulse Signal Acquisition System for Width Information of Pulse Wave.

Authors:  Chuanglu Chen; Zhiqiang Li; Yitao Zhang; Shaolong Zhang; Jiena Hou; Haiying Zhang
Journal:  Sensors (Basel)       Date:  2019-12-18       Impact factor: 3.576

5.  Statistical Analysis of the Consistency of HRV Analysis Using BCG or Pulse Wave Signals.

Authors:  Huiying Cui; Zhongyi Wang; Bin Yu; Fangfang Jiang; Ning Geng; Yongchun Li; Lisheng Xu; Dingchang Zheng; Biyong Zhang; Peilin Lu; Stephen E Greenwald
Journal:  Sensors (Basel)       Date:  2022-03-21       Impact factor: 3.576

  5 in total

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