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| Presentation | 2021-07-05 13:00 Epileptic Spike Detection from Electroencephalogram with Self-Attention Mechanism Kosuke Fukumori (TUAT), Noboru Yoshida, Hidenori Sugano, Madoka Nakajima (Juntendo Univ.), Toshihisa Tanaka (TUAT)  CAS2021-3 VLD2021-3 SIP2021-13 MSS2021-3 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | Automated identification of epileptiform discharges for the diagnosis of epilepsy can mitigate the burden of the exhaustive manual search in electroencephalogram (EEG). Recent studies have indicated that a two-step method that consists of detection of candidate waveforms with signal processing and pattern matching followed by machine learning-based classification is effective. However, the overall performance depends on the detector of candidates. This paper thus considers a scenario without candidate waveforms, that is, we propose a recurrent neural network (RNN)-based self-attention model that can be fitted from the EEG segments generated without detecting spike candidates. In comparison with the state-of-the-art machine learning models which can be applied for EEG classification (LightGBM and EEGNet), the proposed model achieved higher performance (average accuracy: 90.2%). This result strongly suggests that the self-attention mechanism is suitable to an automated identification of the epileptiform discharge in the EEG. |
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| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | epilepsy / epileptiform discharge / neural networks / electroencephalogram (EEG) / / / / | |
| Reference Info. | IEICE Tech. Rep., vol. 121, no. 91, SIP2021-13, pp. 11-15, July 2021. | |
| Paper # | SIP2021-13 | |
| Date of Issue | 2021-06-28 (CAS, VLD, SIP, MSS) | |
| ISSN | Print edition: ISSN 0913-5685 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 | CAS2021-3 VLD2021-3 SIP2021-13 MSS2021-3 |
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| Conference Information | |
| Committee | SIP CAS VLD MSS |
| Conference Date | 2021-07-05 - 2021-07-06 |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Online |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | |
| Paper Information | |
| Registration To | SIP |
| Conference Code | 2021-07-SIP-CAS-VLD-MSS |
| Language | Japanese |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Epileptic Spike Detection from Electroencephalogram with Self-Attention Mechanism |
| Sub Title (in English) | |
| Keyword(1) | epilepsy |
| Keyword(2) | epileptiform discharge |
| Keyword(3) | neural networks |
| Keyword(4) | electroencephalogram (EEG) |
| Keyword(5) | |
| Keyword(6) | |
| Keyword(7) | |
| Keyword(8) | |
| 1st Author's Name | Kosuke Fukumori |
| 1st Author's Affiliation | Tokyo University of Agriculture and Technology (TUAT) |
| 2nd Author's Name | Noboru Yoshida |
| 2nd Author's Affiliation | Juntendo University Nerima Hospital (Juntendo Univ.) |
| 3rd Author's Name | Hidenori Sugano |
| 3rd Author's Affiliation | Juntendo University (Juntendo Univ.) |
| 4th Author's Name | Madoka Nakajima |
| 4th Author's Affiliation | Juntendo University (Juntendo Univ.) |
| 5th Author's Name | Toshihisa Tanaka |
| 5th Author's Affiliation | Tokyo University of Agriculture and Technology (TUAT) |
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| Speaker | 1 |
| Date Time | 2021-07-05 13:00:00 |
| Presentation Time | 25 |
| Registration for | SIP |
| Paper # | CAS2021-3, VLD2021-3, SIP2021-13, MSS2021-3 |
| Volume (vol) | 121 |
| Number (no) | no.89(CAS), no.90(VLD), no.91(SIP), no.92(MSS) |
| Page | pp.11-15 |
| #Pages | 5 |
| Date of Issue | 2021-06-28 (CAS, VLD, SIP, MSS) |
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