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Paper Abstract and Keywords
Presentation 2023-01-20 10:20
Arterial Blood Pressure Waveform Estimation from Photoplethysmogram under Inter-subject Paradigm by U-Net and Domain Adversarial Training
Rikuto Yoshizawa, Kohei Yamamoto, Tomoaki Ohtsuki (Keio) SeMI2022-96
Abstract (in Japanese) (See Japanese page) 
(in English) Blood pressure (BP) estimation methods using photoplethysmogram (PPG) signals based on deep learning models have been actively studied. These methods are also the basis of non-contact BP estimation methods using a camera or a Doppler radar. However, the performance evaluations of many previous studies are under data leakage. To properly evaluate the generalizability of BP estimation models, the evaluation should be performed under the experimental condition that subjects are completely separated between training and test data (inter-subject paradigm). In this research, we propose a BP estimation method from PPG signals under inter-subject paradigm using a subject-distinguishable large public dataset. Our BP estimation method estimates an 8-second BP waveform called arterial BP (ABP) from an 8-second PPG segment using U-Net. From the estimated ABP, systolic BP (SBP), diastolic BP (DBP), and mean BP (MBP) are calculated, which are the discrete single values for the 8-second ABP. In addition, we apply domain adversarial training, which facilitates the BP estimation model to extract subject-invariant features for improving BP estimation accuracy under inter-subject paradigm. Our experimental results showed that our method can estimate BP with moderate accuracy under inter-subject paradigm, particularly MBP. The mean absolute errors for the estimated SBP, DBP, MBP, and ABP were 15.21, 7.12, 8.20, and 10.14 mmHg, respectively. The Pearson’s correlation coefficients between the true and estimated values were 0.49, 0.39, 0.54, and 0.84, respectively.
Keyword (in Japanese) (See Japanese page) 
(in English) Blood pressure / Photoplethysmogram / Deep learning / Inter-subject / Domain adversarial training / / /  
Reference Info. IEICE Tech. Rep., vol. 122, no. 341, SeMI2022-96, pp. 113-118, Jan. 2023.
Paper # SeMI2022-96 
Date of Issue 2023-01-12 (SeMI) 
ISSN Online edition: ISSN 2432-6380
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 SeMI  
Conference Date 2023-01-19 - 2023-01-20 
Place (in Japanese) (See Japanese page) 
Place (in English) Naruto grand hotel 
Topics (in Japanese) (See Japanese page) 
Topics (in English)  
Paper Information
Registration To SeMI 
Conference Code 2023-01-SeMI-SeMI 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Arterial Blood Pressure Waveform Estimation from Photoplethysmogram under Inter-subject Paradigm by U-Net and Domain Adversarial Training 
Sub Title (in English)  
Keyword(1) Blood pressure  
Keyword(2) Photoplethysmogram  
Keyword(3) Deep learning  
Keyword(4) Inter-subject  
Keyword(5) Domain adversarial training  
1st Author's Name Rikuto Yoshizawa  
1st Author's Affiliation Keio University (Keio)
2nd Author's Name Kohei Yamamoto  
2nd Author's Affiliation Keio University (Keio)
3rd Author's Name Tomoaki Ohtsuki  
3rd Author's Affiliation Keio University (Keio)
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Speaker Author-1 
Date Time 2023-01-20 10:20:00 
Presentation Time 10 minutes 
Registration for SeMI 
Paper # SeMI2022-96 
Volume (vol) vol.122 
Number (no) no.341 
Page pp.113-118 
Date of Issue 2023-01-12 (SeMI) 

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