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 |
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) |
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EA2021-96 SIP2021-123 SP2021-81 |
Conference Information |
Committee |
EA SIP SP IPSJ-SLP |
Conference Date |
2022-03-01 - 2022-03-02 |
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(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) |
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(See Japanese page) |
Title (in English) |
Epileptic Seizure Detection Using Active Learning with Riemannian Manifold |
Sub Title (in English) |
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Machine learning |
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Electroencephalogram |
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Epilepsy |
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Domain adaptation |
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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 |
6 |
Date of Issue |
2022-02-22 (EA, SIP, SP) |
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