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Paper Abstract and Keywords
Presentation 2025-03-04 13:25
[Poster Presentation] A dynamic data augmentation method using diffusion models for classification of intensive care EEG
Takuma Bingo, Hajime Yano, Taichiro Ashizaki, Kazuma Koda, Masaya Togo (Kobe Univ.), Riki Matsumoto (Kobe Univ./Kyoto Univ.), Ryoichi Takashima, Tetsuya Takiguchi (Kobe Univ.) EA2024-152 SIP2024-187 SP2024-93
Abstract (in Japanese) (See Japanese page) 
(in English) For patients in intensive care who are unconscious, it is important to quickly determine whether their brains are in a state that could lead to further disorders such as epileptic seizures, and to treat them appropriately. This requires continuous EEGmonitoring, buttheamountofdatamakesitdifficultforaphysicianalonetomonitorall oftheEEGwaveforms. Therefore, automated reading systems are expected to assist in this process. However, the cases in the data of the intensive care EEG are biased, leading to decreases in classification accuracy for the minority classes. In this study, we propose a data augmentation method that uses diffusion models to generate similar data to the original data with uncertain classification, and incorporates them into sequential training. Classification experiments showed that the proposed method improved the classification performance for the minority classes and showed the effectiveness in actual clinical data. In addition, quantitative evaluation results suggest that the diffusion model may be able to generate samples that reflect the specific frequency and temporal characteristics of the EEG.
Keyword (in Japanese) (See Japanese page) 
(in English) Intensive care EEG / Nonconvulsive status epilepticus / Diffusion model / Dynamic data augmentation / / / /  
Reference Info. IEICE Tech. Rep., vol. 124, no. 390, SIP2024-187, pp. 453-458, March 2025.
Paper # SIP2024-187 
Date of Issue 2025-02-23 (EA, SIP, SP) 
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 EA2024-152 SIP2024-187 SP2024-93

Conference Information
Committee EA SIP SP IPSJ-SLP  
Conference Date 2025-03-02 - 2025-03-04 
Place (in Japanese) (See Japanese page) 
Place (in English)  
Topics (in Japanese) (See Japanese page) 
Topics (in English)  
Paper Information
Registration To SIP 
Conference Code 2025-03-EA-SIP-SP-SLP 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) A dynamic data augmentation method using diffusion models for classification of intensive care EEG 
Sub Title (in English)  
Keyword(1) Intensive care EEG  
Keyword(2) Nonconvulsive status epilepticus  
Keyword(3) Diffusion model  
Keyword(4) Dynamic data augmentation  
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1st Author's Name Takuma Bingo  
1st Author's Affiliation Kobe University (Kobe Univ.)
2nd Author's Name Hajime Yano  
2nd Author's Affiliation Kobe University (Kobe Univ.)
3rd Author's Name Taichiro Ashizaki  
3rd Author's Affiliation Kobe University (Kobe Univ.)
4th Author's Name Kazuma Koda  
4th Author's Affiliation Kobe University (Kobe Univ.)
5th Author's Name Masaya Togo  
5th Author's Affiliation Kobe University (Kobe Univ.)
6th Author's Name Riki Matsumoto  
6th Author's Affiliation Kobe University/Kyoto University (Kobe Univ./Kyoto Univ.)
7th Author's Name Ryoichi Takashima  
7th Author's Affiliation Kobe University (Kobe Univ.)
8th Author's Name Tetsuya Takiguchi  
8th Author's Affiliation Kobe University (Kobe Univ.)
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Speaker Author-1 
Date Time 2025-03-04 13:25:00 
Presentation Time 80 minutes 
Registration for SIP 
Paper # EA2024-152, SIP2024-187, SP2024-93 
Volume (vol) vol.124 
Number (no) no.389(EA), no.390(SIP), no.391(SP) 
Page pp.453-458 
#Pages 6 
Date of Issue 2025-02-23 (EA, SIP, SP) 

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