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Paper Abstract and Keywords
Presentation 2022-05-26 14:03
Arterial Blood Pressure Estimation from Electrocardiogram Signals using U-Net
Rikuto Yoshizawa, Kohei Yamamoto, Tomoaki Ohtsuki (Keio Univ.) SeMI2022-5
Abstract (in Japanese) (See Japanese page) 
(in English) Blood pressure estimation methods using electrocardiogram (ECG) signals have been recently studied for user-friendly blood pressure estimation. Previous works proposed deep learning models to estimate blood pressure from ECG signals. However, they can only estimate max, min, and mean blood pressures in about a 10-second segment and cannot estimate the continuous blood pressure transition, called arterial blood pressure (ABP). This report presents the ABP estimation method from ECG signals using the deep learning model of U-Net. Through the performance evaluation with a dataset of about 185 hours of ECG signals, we observed that the proposed method estimated ABP with high accuracy. Furthermore, we confirmed that the accuracies of the calculated max, min, and mean ABPs were comparable to those in the previous works, even though our method can estimate ABP. In the end, we discussed the subject-overfitting problem and future work based on the evaluation of our model and a model proposed in the previous work.
Keyword (in Japanese) (See Japanese page) 
(in English) Blood pressure / Deep learning / ECG / Health monitoring / / / /  
Reference Info. IEICE Tech. Rep., vol. 122, no. 46, SeMI2022-5, pp. 20-25, May 2022.
Paper # SeMI2022-5 
Date of Issue 2022-05-19 (SeMI) 
ISSN Online edition: ISSN 2432-6380
Copyright
and
reproduction		 All rights are reserved and no part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Notwithstanding, instructors are permitted to photocopy isolated articles for noncommercial classroom use without fee. (License No.: 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034)
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Conference Information
Committee SeMI IPSJ-DPS IPSJ-MBL IPSJ-ITS  
Conference Date 2022-05-26 - 2022-05-27 
Place (in Japanese) (See Japanese page) 
Place (in English)  
Topics (in Japanese) (See Japanese page) 
Topics (in English)  
Paper Information
Registration To SeMI 
Conference Code 2022-05-SeMI-DPS-MBL-ITS 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Arterial Blood Pressure Estimation from Electrocardiogram Signals using U-Net 
Sub Title (in English)  
Keyword(1) Blood pressure  
Keyword(2) Deep learning  
Keyword(3) ECG  
Keyword(4) Health monitoring  
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1st Author's Name Rikuto Yoshizawa  
1st Author's Affiliation Keio University (Keio Univ.)
2nd Author's Name Kohei Yamamoto  
2nd Author's Affiliation Keio University (Keio Univ.)
3rd Author's Name Tomoaki Ohtsuki  
3rd Author's Affiliation Keio University (Keio Univ.)
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Speaker Author-1 
Date Time 2022-05-26 14:03:00 
Presentation Time 18 minutes 
Registration for SeMI 
Paper # SeMI2022-5 
Volume (vol) vol.122 
Number (no) no.46 
Page pp.20-25 
#Pages 6 
Date of Issue 2022-05-19 (SeMI) 

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