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Paper Abstract and Keywords
Presentation 2025-10-24 09:25
Deep Learning-Based Multi-Lead ECG Reconstruction Using Lead I and Patient's Metadata
Ryuichi Nakanishi, Akimasa Hirata (Nitech) EMCJ2025-54 MW2025-140 EST2025-67
Abstract (in Japanese) (See Japanese page) 
(in English) This study presents a deep learning model for reconstructing 12-lead electrocardiograms (ECGs) using Lead I and
patient metadata as inputs. The proposed model adopts a dual-branch architecture: one branch processes time-series ECG signals
using a Bi-directional Long Short-Term Memory network, and the other handles clinical metadata. By integrating these features,
the model achieved higher reconstruction accuracy compared to single-lead input alone. Experimental results demonstrated that
incorporating multiple metadata features, particularly axis information and conduction time parameters, contributed significantly
to improved performance. Comparative analysis with existing methods indicated that integrating metadata provides a distinct
advantage in reconstruction performance. Furthermore, uncertainty estimation using Monte Carlo dropout not only enabled
visualization of the model’s confidence for each time point in the reconstructed waveforms but also revealed physiologically
meaningful patterns, such as increased uncertainty around steep QRS complexes and reduced uncertainty in flatter segments.
These findings suggest that the proposed approach offers a promising pathway for enhancing the clinical utility and reliabilityof
wearable ECG systems.
Keyword (in Japanese) (See Japanese page) 
(in English) Electrocardiogram / Wearable device / Deep learning / Bi-LSTM / Monte Carlo dropout / / /  
Reference Info. IEICE Tech. Rep., vol. 125, no. 210, EST2025-67, pp. 83-88, Oct. 2025.
Paper # EST2025-67 
Date of Issue 2025-10-16 (EMCJ, MW, EST) 
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)
Download PDF EMCJ2025-54 MW2025-140 EST2025-67

Conference Information
Committee EMCJ MW EST IEE-EMC  
Conference Date 2025-10-23 - 2025-10-24 
Place (in Japanese) (See Japanese page) 
Place (in English)  
Topics (in Japanese) (See Japanese page) 
Topics (in English)  
Paper Information
Registration To EST 
Conference Code 2025-10-EMCJ-MW-EST-EMC 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Deep Learning-Based Multi-Lead ECG Reconstruction Using Lead I and Patient's Metadata 
Sub Title (in English)  
Keyword(1) Electrocardiogram  
Keyword(2) Wearable device  
Keyword(3) Deep learning  
Keyword(4) Bi-LSTM  
Keyword(5) Monte Carlo dropout  
Keyword(6)  
Keyword(7)  
Keyword(8)  
1st Author's Name Ryuichi Nakanishi  
1st Author's Affiliation Nagoya Institute Technology (Nitech)
2nd Author's Name Akimasa Hirata  
2nd Author's Affiliation Nagoya Institute Technology (Nitech)
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Speaker Author-1 
Date Time 2025-10-24 09:25:00 
Presentation Time 25 minutes 
Registration for EST 
Paper # EMCJ2025-54, MW2025-140, EST2025-67 
Volume (vol) vol.125 
Number (no) no.208(EMCJ), no.209(MW), no.210(EST) 
Page pp.83-88 
#Pages 6 
Date of Issue 2025-10-16 (EMCJ, MW, EST) 

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