Application the intelligent Neural Network to Design the Novel Blood Pressure Monitor

碩士 === 崑山科技大學 === 電子工程研究所 === 99 === Various physiological parameters have been widely used for diseases, prevention and detection, which can observe the occurrence of cardiovascular diseases, through daily measurement of blood pressure. Currently, the most common blood pressure measurement method r...

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Main Authors: Chueh-Yu Chuang, 莊爵譽
Other Authors: 吳崇民
Format: Others
Language:zh-TW
Published: 2011
Online Access:http://ndltd.ncl.edu.tw/handle/50294392061270283000
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spelling ndltd-TW-099KSUT54280012015-10-13T20:18:51Z http://ndltd.ncl.edu.tw/handle/50294392061270283000 Application the intelligent Neural Network to Design the Novel Blood Pressure Monitor 智慧型類神經網路設計之新型血壓計 Chueh-Yu Chuang 莊爵譽 碩士 崑山科技大學 電子工程研究所 99 Various physiological parameters have been widely used for diseases, prevention and detection, which can observe the occurrence of cardiovascular diseases, through daily measurement of blood pressure. Currently, the most common blood pressure measurement method records the pressure on the wrist. The subject might feel uncomfortable and tension of pressure on the arm that it might lead to the measurement error of blood pressure. Electrocardiogram (ECG) represents the electrical activities of the heart functions, which also contains blood pressure-related information. This research in an attempt to extract the related features of blood pressure from the ECG signal. This research developed a new non-invasive blood pressure measurement technology that utilizes the intelligent neural network algorithms to calculate the blood pressure value from the parameters of ECG. The designed blood pressure measurement approach is called the ECG-blood pressure machine. The proposed approach alleviates the errors caused by discomfort, which can provide a feasibility to continuously monitor blood pressure in a less stressful condition. 吳崇民 2011 學位論文 ; thesis 57 zh-TW
collection NDLTD
language zh-TW
format Others
sources NDLTD
description 碩士 === 崑山科技大學 === 電子工程研究所 === 99 === Various physiological parameters have been widely used for diseases, prevention and detection, which can observe the occurrence of cardiovascular diseases, through daily measurement of blood pressure. Currently, the most common blood pressure measurement method records the pressure on the wrist. The subject might feel uncomfortable and tension of pressure on the arm that it might lead to the measurement error of blood pressure. Electrocardiogram (ECG) represents the electrical activities of the heart functions, which also contains blood pressure-related information. This research in an attempt to extract the related features of blood pressure from the ECG signal. This research developed a new non-invasive blood pressure measurement technology that utilizes the intelligent neural network algorithms to calculate the blood pressure value from the parameters of ECG. The designed blood pressure measurement approach is called the ECG-blood pressure machine. The proposed approach alleviates the errors caused by discomfort, which can provide a feasibility to continuously monitor blood pressure in a less stressful condition.
author2 吳崇民
author_facet 吳崇民
Chueh-Yu Chuang
莊爵譽
author Chueh-Yu Chuang
莊爵譽
spellingShingle Chueh-Yu Chuang
莊爵譽
Application the intelligent Neural Network to Design the Novel Blood Pressure Monitor
author_sort Chueh-Yu Chuang
title Application the intelligent Neural Network to Design the Novel Blood Pressure Monitor
title_short Application the intelligent Neural Network to Design the Novel Blood Pressure Monitor
title_full Application the intelligent Neural Network to Design the Novel Blood Pressure Monitor
title_fullStr Application the intelligent Neural Network to Design the Novel Blood Pressure Monitor
title_full_unstemmed Application the intelligent Neural Network to Design the Novel Blood Pressure Monitor
title_sort application the intelligent neural network to design the novel blood pressure monitor
publishDate 2011
url http://ndltd.ncl.edu.tw/handle/50294392061270283000
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