Young Researcher Paper Award 2023
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Print: ISSN 0914-4935
Online: ISSN 2435-0869
Sensors and Materials
is an international peer-reviewed open access journal to provide a forum for researchers working in multidisciplinary fields of sensing technology.
Online: ISSN 2435-0869
Sensors and Materials
is an international peer-reviewed open access journal to provide a forum for researchers working in multidisciplinary fields of sensing technology.
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Sensors and Materials
is covered by Science Citation Index Expanded (Clarivate Analytics), Scopus (Elsevier), and other databases.
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is covered by Science Citation Index Expanded (Clarivate Analytics), Scopus (Elsevier), and other databases.
Instructions to authors
English 日本語
Instructions for manuscript preparation
English 日本語
Template
English
Publisher
MYU K.K.
Sensors and Materials
1-23-3-303 Sendagi,
Bunkyo-ku, Tokyo 113-0022, Japan
Tel: 81-3-3827-8549
Fax: 81-3-3827-8547
MYU Research, a scientific publisher, seeks a native English-speaking proofreader with a scientific background. B.Sc. or higher degree is desirable. In-office position; work hours negotiable. Call 03-3827-8549 for further information.
MYU Research
(proofreading and recording)
MYU K.K.
(translation service)
The Art of Writing Scientific Papers
(How to write scientific papers)
(Japanese Only)
Sensors and Materials, Volume 35, Number 3(4) (2023)
Copyright(C) MYU K.K.
Copyright(C) MYU K.K.
|
pp. 1081-1088
S&M3230 Research Paper of Special Issue https://doi.org/10.18494/SAM4234 Published: March 31, 2023 Artificial Intelligence Model for an Electrocardiography-based Blood Pressure Estimation System [PDF] Chung-Min Wu, Shih-Chung Chen, and Yeou-Jiunn Chen (Received July 30, 2022; Accepted February 24, 2023) Keywords: artificial intelligence, electrocardiography, blood pressure, systolic blood pressure, diastolic blood pressure, convolutional neural network
In this study, we propose a novel artificial intelligence model for blood pressure estimation that establishes a method to estimate both systolic and diastolic blood pressures based on an electrocardiogram. Experimental results show that the root mean square errors for systolic and diastolic blood pressures are 3.82 and 2.17, respectively. Therefore, the proposed approach complies with the Association for the Advancement of Medical Instrumentation standard. The proposed structure is feasible and can be implemented by being integrated with electrode sensors and a signal processing platform. In the future, this technology can replace home care systems or wearable devices to provide warnings of health issues.
Corresponding author: Yeou-Jiunn Chen  This work is licensed under a Creative Commons Attribution 4.0 International License. Cite this article Chung-Min Wu, Shih-Chung Chen, and Yeou-Jiunn Chen, Artificial Intelligence Model for an Electrocardiography-based Blood Pressure Estimation System, Sens. Mater., Vol. 35, No. 3, 2023, p. 1081-1088. |
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