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Paper Abstract and Keywords
Presentation 2024-12-07 11:10
Identification of Spatio-Temporal Filters in the Neural Network Models Trained as Epileptic Electrocorticogram Detectors
Daisuke Suekane, Moemi Yamaji, Shinjiro Yamamasu, Yuki Hayashida (Mie Univ.) MBE2024-20
Abstract (in Japanese) (See Japanese page) 
(in English) Responsive Neuro-stimulation, which detects abnormal brain activity recorded through electrodes placed on the brain surface and provides electrical stimulation via electrodes implanted at targeted sites within the brain, offers a promising treatment option for patients with intractable epilepsy who do not respond to medication. Recently, advanced seizure detection algorithms utilizing machine learning approaches have been developed. However, there is significant variability in detection accuracy across patients, and the reasons for this remain unclear. In this study, we applied white noise analysis to patient-specific Convolutional Neural Network (CNN) models trained on each epilepsy patient to extract spatio-temporal filters acquired within the models. The analysis revealed insights into the frequency spectrum of electrocorticogram signals and temporal activity changes between electrode channels that the CNN model use for seizure detection. These findings are expected to contribute to the development of neural network models with enhanced seizure detection capabilities in the future.
Keyword (in Japanese) (See Japanese page) 
(in English) Epilepsy / Responsive Neuro-Stimulation / Electrocorticogram Electrodes / White Noise Analysis / Cross Correlation / Convolutional Neural Network / /  
Reference Info. IEICE Tech. Rep., vol. 124, no. 296, MBE2024-20, pp. 20-25, Dec. 2024.
Paper # MBE2024-20 
Date of Issue 2024-11-30 (MBE) 
ISSN 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 MBE2024-20

Conference Information
Committee MBE  
Conference Date 2024-12-07 - 2024-12-07 
Place (in Japanese) (See Japanese page) 
Place (in English) KINDAI University 
Topics (in Japanese) (See Japanese page) 
Topics (in English) ME, etc. 
Paper Information
Registration To MBE 
Conference Code 2024-12-MBE 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Identification of Spatio-Temporal Filters in the Neural Network Models Trained as Epileptic Electrocorticogram Detectors 
Sub Title (in English)  
Keyword(1) Epilepsy  
Keyword(2) Responsive Neuro-Stimulation  
Keyword(3) Electrocorticogram Electrodes  
Keyword(4) White Noise Analysis  
Keyword(5) Cross Correlation  
Keyword(6) Convolutional Neural Network  
Keyword(7)  
Keyword(8)  
1st Author's Name Daisuke Suekane  
1st Author's Affiliation Mie University (Mie Univ.)
2nd Author's Name Moemi Yamaji  
2nd Author's Affiliation Mie University (Mie Univ.)
3rd Author's Name Shinjiro Yamamasu  
3rd Author's Affiliation Mie University (Mie Univ.)
4th Author's Name Yuki Hayashida  
4th Author's Affiliation Mie University (Mie Univ.)
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Speaker Author-1 
Date Time 2024-12-07 11:10:00 
Presentation Time 25 minutes 
Registration for MBE 
Paper # MBE2024-20 
Volume (vol) vol.124 
Number (no) no.296 
Page pp.20-25 
#Pages 6 
Date of Issue 2024-11-30 (MBE) 

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