The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
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The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
A pressão arterial é a medida da força exercida pelo sangue contra as paredes das artérias. A hipertensão é um importante fator de risco de doenças cardiovasculares. As pressões arteriais sistólica e diastólica obtidas pelo método oscilométrico podem trazer pistas sobre hipertensão. No entanto, a pressão arterial é influenciada por características individuais como a fisiologia, a geometria do coração, a figura corporal, o sexo e a idade. Portanto, a consideração das características individuais é um requisito para um monitoramento confiável da hipertensão. As formas de onda de oscilação extraídas da pressão do manguito refletem características individuais em termos de padrões de oscilação que variam em tamanho e amplitude ao longo do tempo. Assim, características uniformes para características individuais dos padrões de oscilação foram extraídas e aplicadas para avaliar as pressões arteriais sistólica e diastólica usando duas redes neurais feedforward. As medidas das pressões arteriais sistólica e diastólica de duas redes neurais foram comparadas com os valores médios das pressões arteriais sistólica e diastólica obtidas por duas enfermeiras pelo método auscultatório. O desempenho do reconhecimento baseou-se na diferença entre as pressões arteriais medidas pelo método de ausculta e o método proposto com duas redes neurais. O desempenho de reconhecimento da pressão arterial sistólica foi de 98.2% para
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Youngsuk SHIN, "Estimation of Blood Pressure Measurements for Hypertension Diagnosis Using Oscillometric Method" in IEICE TRANSACTIONS on Fundamentals,
vol. E94-A, no. 2, pp. 806-812, February 2011, doi: 10.1587/transfun.E94.A.806.
Abstract: Blood pressure is the measurement of the force exerted by blood against the walls of the arteries. Hypertension is a major risk factor of cardiovascular diseases. The systolic and diastolic blood pressures obtained from the oscillometric method could carry clues about hypertension. However, blood pressure is influenced by individual traits such as physiology, the geometry of the heart, body figure, gender and age. Therefore, consideration of individual traits is a requisite for reliable hypertension monitoring. The oscillation waveforms extracted from the cuff pressure reflect individual traits in terms of oscillation patterns that vary in size and amplitude over time. Thus, uniform features for individual traits from the oscillation patterns were extracted, and they were applied to evaluate systolic and diastolic blood pressures using two feedforward neural networks. The measurements of systolic and diastolic blood pressures from two neural networks were compared with the average values of systolic and diastolic blood pressures obtained by two nurses using the auscultatory method. The recognition performance was based on the difference between the blood pressures measured by the auscultation method and the proposed method with two neural networks. The recognition performance for systolic blood pressure was found to be 98.2% for
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/transfun.E94.A.806/_p
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@ARTICLE{e94-a_2_806,
author={Youngsuk SHIN, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Estimation of Blood Pressure Measurements for Hypertension Diagnosis Using Oscillometric Method},
year={2011},
volume={E94-A},
number={2},
pages={806-812},
abstract={Blood pressure is the measurement of the force exerted by blood against the walls of the arteries. Hypertension is a major risk factor of cardiovascular diseases. The systolic and diastolic blood pressures obtained from the oscillometric method could carry clues about hypertension. However, blood pressure is influenced by individual traits such as physiology, the geometry of the heart, body figure, gender and age. Therefore, consideration of individual traits is a requisite for reliable hypertension monitoring. The oscillation waveforms extracted from the cuff pressure reflect individual traits in terms of oscillation patterns that vary in size and amplitude over time. Thus, uniform features for individual traits from the oscillation patterns were extracted, and they were applied to evaluate systolic and diastolic blood pressures using two feedforward neural networks. The measurements of systolic and diastolic blood pressures from two neural networks were compared with the average values of systolic and diastolic blood pressures obtained by two nurses using the auscultatory method. The recognition performance was based on the difference between the blood pressures measured by the auscultation method and the proposed method with two neural networks. The recognition performance for systolic blood pressure was found to be 98.2% for
keywords={},
doi={10.1587/transfun.E94.A.806},
ISSN={1745-1337},
month={February},}
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TY - JOUR
TI - Estimation of Blood Pressure Measurements for Hypertension Diagnosis Using Oscillometric Method
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 806
EP - 812
AU - Youngsuk SHIN
PY - 2011
DO - 10.1587/transfun.E94.A.806
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E94-A
IS - 2
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - February 2011
AB - Blood pressure is the measurement of the force exerted by blood against the walls of the arteries. Hypertension is a major risk factor of cardiovascular diseases. The systolic and diastolic blood pressures obtained from the oscillometric method could carry clues about hypertension. However, blood pressure is influenced by individual traits such as physiology, the geometry of the heart, body figure, gender and age. Therefore, consideration of individual traits is a requisite for reliable hypertension monitoring. The oscillation waveforms extracted from the cuff pressure reflect individual traits in terms of oscillation patterns that vary in size and amplitude over time. Thus, uniform features for individual traits from the oscillation patterns were extracted, and they were applied to evaluate systolic and diastolic blood pressures using two feedforward neural networks. The measurements of systolic and diastolic blood pressures from two neural networks were compared with the average values of systolic and diastolic blood pressures obtained by two nurses using the auscultatory method. The recognition performance was based on the difference between the blood pressures measured by the auscultation method and the proposed method with two neural networks. The recognition performance for systolic blood pressure was found to be 98.2% for
ER -