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A New Approach for Blood Pressure Monitoring based on ECG and PPG Signals by using Artihcial Neural Networks

by Younessi Heravi Mohammad Amin, Maghooli Keivan, Joharinia Sima
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 103 - Number 12
Year of Publication: 2014
Authors: Younessi Heravi Mohammad Amin, Maghooli Keivan, Joharinia Sima
10.5120/18129-9225

Younessi Heravi Mohammad Amin, Maghooli Keivan, Joharinia Sima . A New Approach for Blood Pressure Monitoring based on ECG and PPG Signals by using Artihcial Neural Networks. International Journal of Computer Applications. 103, 12 ( October 2014), 36-40. DOI=10.5120/18129-9225

@article{ 10.5120/18129-9225,
author = { Younessi Heravi Mohammad Amin, Maghooli Keivan, Joharinia Sima },
title = { A New Approach for Blood Pressure Monitoring based on ECG and PPG Signals by using Artihcial Neural Networks },
journal = { International Journal of Computer Applications },
issue_date = { October 2014 },
volume = { 103 },
number = { 12 },
month = { October },
year = { 2014 },
issn = { 0975-8887 },
pages = { 36-40 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume103/number12/18129-9225/ },
doi = { 10.5120/18129-9225 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:34:24.482547+05:30
%A Younessi Heravi Mohammad Amin
%A Maghooli Keivan
%A Joharinia Sima
%T A New Approach for Blood Pressure Monitoring based on ECG and PPG Signals by using Artihcial Neural Networks
%J International Journal of Computer Applications
%@ 0975-8887
%V 103
%N 12
%P 36-40
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Background: Pulse transit time has been demonstrated as one of the potential parameters for a cuffless blood pressure measurement. The accuracy of this method depends on the initial calibration that is obtained by several measurements. The aim of this study was to employ artificial neural network in order to estimate the blood pressure based on PTT. PTT is de?ned as the time delay between the R-wave of the ECG and the peak of the pulse wave in the ?nger. To train the ANN for modeling the blood pressure, this study used a database containing 65 subjects. For each subject, BP was taken several times in different condition. The trained ANN was capable of establishing a function between the PTT and the BP as an input and a response, respectively. The results of estimating BP were compared with the results of sphygmomanometer method and the error rate was calculated. The absolute error and error percentage in systolic blood pressure between cuff method and the present method were 5. 41±2. 63 mmHg, 4. 09±1. 59% and for diastolic blood pressure were 7. 01±2. 52 mmHg, 6. 88±2. 43%. The results indicated that the BP measurement by cuff method and BP predicted with trained ANN differ by only less than 10%. It is obvious that the neural network prediction fit properly to the cuff results. The results of proposed method were closely in agreement with the results of the sphygmomanometer cuff. So the present method could be applied as an effective tool for the blood pressure estimation.

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Index Terms

Computer Science
Information Sciences

Keywords

Blood Pressure monitoring Pulse transit time Arti?cial neural network.

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