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Paper Abstract and Keywords
Presentation 2026-03-16 13:25
Arrhythmia Risk Assessment in Patients with Hypertrophic Cardiomyopathy Using Fine-Tuning of Deep Learning Models
Sho Akada (Okayama Univ.), Shohei Hara, Kazufumi Nakamura, Shinsuke Yuasa (Okayama Univ. Hospital), Tsuyoshi Migita, Norikazu Takahashi (Okayama Univ.) NC2025-95
Abstract (in Japanese) (See Japanese page) 
(in English) We propose a deep learning method that makes use of ensemble learning based on cross-validation and transfer learning to address the task of predicting the future risk of life-threatening ventricular arrhythmias in patients with hypertrophic cardiomyopathy (HCM) from their electrocardiograms (ECGs). In the proposed method, the ECG data of patients with HCM are divided into five subsets using the idea of cross validation to generate five datasets, from which five separate models are trained. During inference, the final prediction is obtained by averaging the outputs of these five models. Furthermore, a model pretrained on a large-scale ECG dataset is employed as the initial model. While preserving the low-level representations responsible for extracting general features, the high-level parameters are fine-tuned using ECG data from patients with HCM. The results demonstrate that the proposed method enables efficient learning of ECG characteristics specific to HCM even with a limited number of cases, and achieves improved predictive performance and training stability compared with models trained from scratch.
Keyword (in Japanese) (See Japanese page) 
(in English) deep learning / electrocardiogram / cross-validation / ensemble learning / transfer learning / fine-tuning / /  
Reference Info. IEICE Tech. Rep., vol. 125, no. 413, NC2025-95, pp. 24-29, March 2026.
Paper # NC2025-95 
Date of Issue 2026-03-09 (NC) 
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 NC2025-95

Conference Information
Committee NC MBE  
Conference Date 2026-03-16 - 2026-03-18 
Place (in Japanese) (See Japanese page) 
Place (in English) The University of Tokyo 
Topics (in Japanese) (See Japanese page) 
Topics (in English) General 
Paper Information
Registration To NC 
Conference Code 2026-03-NC-MBE 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Arrhythmia Risk Assessment in Patients with Hypertrophic Cardiomyopathy Using Fine-Tuning of Deep Learning Models 
Sub Title (in English)  
Keyword(1) deep learning  
Keyword(2) electrocardiogram  
Keyword(3) cross-validation  
Keyword(4) ensemble learning  
Keyword(5) transfer learning  
Keyword(6) fine-tuning  
Keyword(7)  
Keyword(8)  
1st Author's Name Sho Akada  
1st Author's Affiliation Okayama University (Okayama Univ.)
2nd Author's Name Shohei Hara  
2nd Author's Affiliation Okayama University Hospital (Okayama Univ. Hospital)
3rd Author's Name Kazufumi Nakamura  
3rd Author's Affiliation Okayama University Hospital (Okayama Univ. Hospital)
4th Author's Name Shinsuke Yuasa  
4th Author's Affiliation Okayama University Hospital (Okayama Univ. Hospital)
5th Author's Name Tsuyoshi Migita  
5th Author's Affiliation Okayama University (Okayama Univ.)
6th Author's Name Norikazu Takahashi  
6th Author's Affiliation Okayama University (Okayama Univ.)
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Speaker Author-1 
Date Time 2026-03-16 13:25:00 
Presentation Time 25 minutes 
Registration for NC 
Paper # NC2025-95 
Volume (vol) vol.125 
Number (no) no.413 
Page pp.24-29 
#Pages 6 
Date of Issue 2026-03-09 (NC) 

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