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Paper Abstract and Keywords
Presentation 2020-03-03 09:00
[Poster Presentation] EpiNet: Convolutional Neural Network for Epileptic Seizure Localization from Interictal Intracranial EEG
Kosuke Mori, Kosuke Fukumori, Toshihisa Tanaka (TUAT), Yasushi Iimura, Takumi Mitsuhashi, Hidenori Sugano (Juntendo Univ.) EA2019-157 SIP2019-159 SP2019-106
Abstract (in Japanese) (See Japanese page) 
(in English) The electroencephalogram (EEG) recording is necessary for epileptic diagnosis. In particular, the intracranial EEG (iEEG) data is essential to localize the seizure onset zone (SOZ). However, it is time-consuming and heavy load for specialists to interpret the iEEG recorded for long time. Therefore, an automatic technology to localize the SOZ is required. Based on VGG, the well-known convolutional neural network for image recognition, we propose a model for analyzing one-dimensional signals and try to localize the SOZ. For this model, the input is a signal obtained by dividing the iEEG in a short time, and the output is whether the recording area of the iEEG is the SOZ or not. Class-Balanced Focal Loss, which is reported to be effective for imbalanced data, was used as the loss function for training. We conducted time-series split cross-validation for the iEEG from cortical dysplasia epilepsy patients. As a result, the proposed model achieved generally higher AUC, F-measure, sensitivity, and specificity than the conventional method using feature extraction and SVM. In consequence, it is possible to outperform the conventional method by an appropriate neural network and loss function.
Keyword (in Japanese) (See Japanese page) 
(in English) Epilepsy / Seizure onset zone / Electroencephalogram / Supervised learning / Convolutional Neural Network / / /  
Reference Info. IEICE Tech. Rep., vol. 119, no. 440, SIP2019-159, pp. 325-330, March 2020.
Paper # SIP2019-159 
Date of Issue 2020-02-24 (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 EA2019-157 SIP2019-159 SP2019-106

Conference Information
Committee SP EA SIP  
Conference Date 2020-03-02 - 2020-03-03 
Place (in Japanese) (See Japanese page) 
Place (in English) Okinawa Industry Support Center 
Topics (in Japanese) (See Japanese page) 
Topics (in English)  
Paper Information
Registration To SIP 
Conference Code 2020-03-SP-EA-SIP 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) EpiNet: Convolutional Neural Network for Epileptic Seizure Localization from Interictal Intracranial EEG 
Sub Title (in English)  
Keyword(1) Epilepsy  
Keyword(2) Seizure onset zone  
Keyword(3) Electroencephalogram  
Keyword(4) Supervised learning  
Keyword(5) Convolutional Neural Network  
1st Author's Name Kosuke Mori  
1st Author's Affiliation Tokyo University of Agriculture and Technology (TUAT)
2nd Author's Name Kosuke Fukumori  
2nd Author's Affiliation Tokyo University of Agriculture and Technology (TUAT)
3rd Author's Name Toshihisa Tanaka  
3rd Author's Affiliation Tokyo University of Agriculture and Technology (TUAT)
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 2020-03-03 09:00:00 
Presentation Time 90 minutes 
Registration for SIP 
Paper # EA2019-157, SIP2019-159, SP2019-106 
Volume (vol) vol.119 
Number (no) no.439(EA), no.440(SIP), no.441(SP) 
Page pp.325-330 
Date of Issue 2020-02-24 (EA, SIP, SP) 

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