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Paper Abstract and Keywords
Presentation 2020-03-03 09:00
[Poster Presentation] Comparison of Neural Network Models for Detection of Spatiotemporal Abnormal Intervals in Epileptic EEG
Kosuke Fukumori (TUAT), Noboru Yoshida (Juntendo Univ.), Toshihisa Tanaka (TUAT) EA2019-156 SIP2019-158 SP2019-105
Abstract (in Japanese) (See Japanese page) 
(in English) Epilepsy is a chronic brain disease, and the detection of abnormal waveforms by scalp electroencephalography (EEG) is an important step in in diagnosing.
Since the manual analyzation takes much time labor, the establishment of automatic diagnosis support technology is required.
In recent years, the effectiveness of machine learning has been raised, however verification of various epileptic EEGs is not enough.
In this paper, we detect abnormal interval automatically in EEG of children and juvenile epilepsy where abnormal waveforms appear continuously.
As a basic study, we compared a supervised model with learned labels annotated by a specialist and an unsupervised model without supervised labels.
In the supervised model, a convolutional neural network (CNN) is constructed, and in the unsupervised model, a variational auto-encoder based on CNN is constructed.
In the experiment, we performed a task to detect abnormal sections using using the scalp EEG of eight children and juvenile absence epilepsy patients.
As a result, the learning model with the the label indicating the abnormal interval showed high detection performance (up to AUC > 0.99) in 6 out of 8 cases.
This result suggests that the labels are extremely important information for training a learning model in detecting abnormal EEG.
Keyword (in Japanese) (See Japanese page) 
(in English) epilepsy / abnormal intervals / supervised learning / unsupervised learning / electroencephalogram (EEG) / / /  
Reference Info. IEICE Tech. Rep., vol. 119, no. 440, SIP2019-158, pp. 319-323, March 2020.
Paper # SIP2019-158 
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 EA2019-156 SIP2019-158 SP2019-105

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) Comparison of Neural Network Models for Detection of Spatiotemporal Abnormal Intervals in Epileptic EEG 
Sub Title (in English)  
Keyword(1) epilepsy  
Keyword(2) abnormal intervals  
Keyword(3) supervised learning  
Keyword(4) unsupervised learning  
Keyword(5) 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 Toshihisa Tanaka  
3rd Author's Affiliation Tokyo University of Agriculture and Technology (TUAT)
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Date Time 2020-03-03 09:00:00 
Presentation Time 90 
Registration for SIP 
Paper # EA2019-156, SIP2019-158, SP2019-105 
Volume (vol) 119 
Number (no) no.439(EA), no.440(SIP), no.441(SP) 
Page pp.319-323 
Date of Issue 2020-02-24 (EA, SIP, SP) 

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