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Paper Abstract and Keywords
Presentation 2022-03-02 15:35
[Poster Presentation] Epileptic Seizure Detection Using Active Learning with Riemannian Manifold
Toshiki Orihara, Toshihisa Tanaka (TUAT) EA2021-96 SIP2021-123 SP2021-81
Abstract (in Japanese) (See Japanese page) 
(in English) In order to realize machine learning for diagnosis, it is necessary to solve the problem that the training model is not always effective for unknown data (domain adaptation problem) because the distribution of data is different for each patient. In this paper, we apply the unsupervised domain adaptation method by translation on a riemannian manifold to seizure detection for EEG of epilepsy patients. We propose a mechanism to actively train the model by translating both the source and target data to the centre point of the source domain, which is close to the riemannian distance from the target domain. We conducted inter-patient validation using a public dataset including seizure EEG, and confirmed that the proposed method can detect seizure EEG with high AUC and correct answer rate. It is suggested that the proposed method can be applied not only to seizure detection for EEG but also to the identification of EEG with domain shift in general.
Keyword (in Japanese) (See Japanese page) 
(in English) Machine learning / Electroencephalogram / Epilepsy / Domain adaptation / Riemannian manifold / / /  
Reference Info. IEICE Tech. Rep., vol. 121, no. 384, SIP2021-123, pp. 201-206, March 2022.
Paper # SIP2021-123 
Date of Issue 2022-02-22 (EA, SIP, SP) 
ISSN Online edition: ISSN 2432-6380
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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 EA2021-96 SIP2021-123 SP2021-81

Conference Information
Committee EA SIP SP IPSJ-SLP  
Conference Date 2022-03-01 - 2022-03-02 
Place (in Japanese) (See Japanese page) 
Place (in English)  
Topics (in Japanese) (See Japanese page) 
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Paper Information
Registration To SIP 
Conference Code 2022-03-EA-SIP-SP-SLP 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Epileptic Seizure Detection Using Active Learning with Riemannian Manifold 
Sub Title (in English)  
Keyword(1) Machine learning  
Keyword(2) Electroencephalogram  
Keyword(3) Epilepsy  
Keyword(4) Domain adaptation  
Keyword(5) Riemannian manifold  
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1st Author's Name Toshiki Orihara  
1st Author's Affiliation Tokyo University of Agriculture and Technology (TUAT)
2nd Author's Name Toshihisa Tanaka  
2nd Author's Affiliation Tokyo University of Agriculture and Technology (TUAT)
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Speaker Author-1 
Date Time 2022-03-02 15:35:00 
Presentation Time 120 minutes 
Registration for SIP 
Paper # EA2021-96, SIP2021-123, SP2021-81 
Volume (vol) vol.121 
Number (no) no.383(EA), no.384(SIP), no.385(SP) 
Page pp.201-206 
#Pages
Date of Issue 2022-02-22 (EA, SIP, SP) 


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