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Paper Abstract and Keywords
Presentation 2019-03-15 13:30
[Poster Presentation] Epileptic Focus Detection from Interictal Electroencephalogram using RNN
Byambadorj Nyamradnaa, Kosuke Fukumori, Toshihisa Tanaka (TAT), Yasushi Iimura, Takumi Mitsuhashi, Hidenori Sugano (Juntendo Univ.) EA2018-138 SIP2018-144 SP2018-100
Abstract (in Japanese) (See Japanese page) 
(in English) Epilepsy is a neurological disorder characterized by recurrent seizures. An option of treatment is surgical resection of the brain tissue responsible for seizures. However, this option depends critically on accurate localization of the pathological brain tissue, which is referred to as the epileptic focus(seizure onset zone; SOZ). To this end, it is necessary to localize the SOZ causing seizures from the electrocorticogram (ECoG) signals. Since the inspection of ECoG recorded for a long time is a heavy load for clinicians, a technology for automatic detection of the epileptic focus from interictal ECoG is required. Therefore, We propose a method to detect epileptic focus using supervised learning from Interictal Electrocorticogram of epileptic patients. As a machine learning model, BiLSTM model which is one kind of Recurrent neural network is adopted. In addition, we compare performance with logistic regression model, SVM model as comparative models. When the epileptic focus was detected in a model learned for the interictal ECoG of a cortical dysplasia epilepsy patient, a high discrimination rate was achieved.
Keyword (in Japanese) (See Japanese page) 
(in English) Epilepsy / Seizure onset zone / Electrocorticogram / Supervised learning / Recurrent Neural Network / / /  
Reference Info. IEICE Tech. Rep., vol. 118, no. 496, SIP2018-144, pp. 229-231, March 2019.
Paper # SIP2018-144 
Date of Issue 2019-03-07 (EA, SIP, SP) 
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 EA2018-138 SIP2018-144 SP2018-100

Conference Information
Committee EA SIP SP  
Conference Date 2019-03-14 - 2019-03-15 
Place (in Japanese) (See Japanese page) 
Place (in English) i+Land nagasaki (Nagasaki-shi) 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Engineering/Electro Acoustics, Signal Processing, Speech, and Related Topics 
Paper Information
Registration To SIP 
Conference Code 2019-03-EA-SIP-SP 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Epileptic Focus Detection from Interictal Electroencephalogram using RNN 
Sub Title (in English)  
Keyword(1) Epilepsy  
Keyword(2) Seizure onset zone  
Keyword(3) Electrocorticogram  
Keyword(4) Supervised learning  
Keyword(5) Recurrent Neural Network  
1st Author's Name Byambadorj Nyamradnaa  
1st Author's Affiliation Tokyo University of Agriculture and Technology (TAT)
2nd Author's Name Kosuke Fukumori  
2nd Author's Affiliation Tokyo University of Agriculture and Technology (TAT)
3rd Author's Name Toshihisa Tanaka  
3rd Author's Affiliation Tokyo University of Agriculture and Technology (TAT)
4th Author's Name Yasushi Iimura  
4th Author's Affiliation Juntendo University (Juntendo Univ.)
5th Author's Name Takumi Mitsuhashi  
5th Author's Affiliation Juntendo University (Juntendo Univ.)
6th Author's Name Hidenori Sugano  
6th Author's Affiliation Juntendo University (Juntendo Univ.)
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Speaker Author-1 
Date Time 2019-03-15 13:30:00 
Presentation Time 90 minutes 
Registration for SIP 
Paper # EA2018-138, SIP2018-144, SP2018-100 
Volume (vol) vol.118 
Number (no) no.495(EA), no.496(SIP), no.497(SP) 
Page pp.229-231 
Date of Issue 2019-03-07 (EA, SIP, SP) 

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