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Paper Abstract and Keywords
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
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.
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
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

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)  
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 Author-1 
Date Time 2021-07-05 13:00:00 
Presentation Time 25 minutes 
Registration for SIP 
Paper # CAS2021-3, VLD2021-3, SIP2021-13, MSS2021-3 
Volume (vol) vol.121 
Number (no) no.89(CAS), no.90(VLD), no.91(SIP), no.92(MSS) 
Page pp.11-15 
Date of Issue 2021-06-28 (CAS, VLD, SIP, MSS) 

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