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| Presentation | 2019-03-15 13:30 [Poster Presentation] Epileptic Spike Detection and Identification of Effective Frequency Band with Neural Networks Kosuke Fukumori (TUAT), Noboru Yoshida (Juntendo Univ.), Toshihisa Tanaka (TUAT)  EA2018-139 SIP2018-145 SP2018-101 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | Epilepsy is a complex neurological disorder and can lead to an adverse impact on an individual's cognitive functions. In diagnosis epilepsy, paroxysmal spikes are frequently recorded in the electroencephalogram (EEG) of epileptic patients. Recently, several methods for automatic spike detection have gradually raised in popularity and usage. As a typical method, machine learning models are used with discrete wavelet transform or a bank of filters as the preprocessor. In this study, we propose a method for identifying the frequency band of interest from the target EEG using a convolutional neural network. With the verification experiment, a traditional preprocessing method and the proposed method are compared. As a result, the proposed method achieves almost comparable performance to that achieved in the traditional preprocessing. Moreover, the filters of the proposed method emphasize the lower frequency band (approximately 8--16 Hz). |
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| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | epilepsy / spike detection / data-driven / neural networks / electroencephalogram (EEG) / / / | |
| Reference Info. | IEICE Tech. Rep., vol. 118, no. 496, SIP2018-145, pp. 233-235, March 2019. | |
| Paper # | SIP2018-145 | |
| Date of Issue | 2019-03-07 (EA, SIP, SP) | |
| 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 | EA2018-139 SIP2018-145 SP2018-101 |
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| 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 Spike Detection and Identification of Effective Frequency Band with Neural Networks |
| Sub Title (in English) | |
| Keyword(1) | epilepsy |
| Keyword(2) | spike detection |
| Keyword(3) | data-driven |
| Keyword(4) | neural networks |
| Keyword(5) | electroencephalogram (EEG) |
| 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 | Toshihisa Tanaka |
| 3rd Author's Affiliation | Tokyo University of Agriculture and Technology (TUAT) |
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| Speaker | Author-1 |
| Date Time | 2019-03-15 13:30:00 |
| Presentation Time | 90 minutes |
| Registration for | SIP |
| Paper # | EA2018-139, SIP2018-145, SP2018-101 |
| Volume (vol) | vol.118 |
| Number (no) | no.495(EA), no.496(SIP), no.497(SP) |
| Page | pp.233-235 |
| #Pages | 3 |
| Date of Issue | 2019-03-07 (EA, SIP, SP) |
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