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Paper Abstract and Keywords
Presentation 2023-03-15 16:15
Correlation dimensions of EEG time series characterizing sleep stages in mice
Kazuki Koyama (Rikkyo Univ.), Masanori Sakaguchi (Univ. Tsukuba), Takaaki Ohnishi (Rikkyo Univ.) MSS2022-77 NLP2022-122
Abstract (in Japanese) (See Japanese page) 
(in English) In this study, we estimate the embedding dimension, which is necessary for embedding EEG time series of mouse sleep into higher dimensions. Taking the mouse sleep stages labeled as four stages into consideration, a correlation dimension of the longest consecutive time series data for each sleep stage label is obtained by the GP method. An appropriate embedding dimension was estimated from the correlation dimension and compared with the randomly shuffled time series with various time delays. As a result of the comparison, the time delay is estimated at most 4, because the larger the value of time delay, the closer the correlation dimension of the analyzed time series is to the value of the randomly shuffled time series. Dimensionality estimation using False Nearest Neighbor algorithm revealed a large difference in convergence between the time series with a time delay of 1 and those with other time delays. When False Nearest Neighbor algorithm was applied to the randomly shuffled time series, it was confirmed that, as with the GP algorithm, the convergence of the time series with larger time delay was similar to that of the randomly shuffled time series. The results of False Nearest Neighbor algorithm suggest that the number
of data was too small to estimate the correlation dimension.
Keyword (in Japanese) (See Japanese page) 
(in English) Non-Linear Time Series Analysis / Correlation Dimension / GP Algorithm / FNN Algorithm / Sleep EEG Data / Mouse / /  
Reference Info. IEICE Tech. Rep., vol. 122, no. 436, NLP2022-122, pp. 75-80, March 2023.
Paper # NLP2022-122 
Date of Issue 2023-03-08 (MSS, NLP) 
ISSN Online edition: ISSN 2432-6380
Copyright
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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 MSS2022-77 NLP2022-122

Conference Information
Committee NLP MSS  
Conference Date 2023-03-15 - 2023-03-17 
Place (in Japanese) (See Japanese page) 
Place (in English)  
Topics (in Japanese) (See Japanese page) 
Topics (in English)  
Paper Information
Registration To NLP 
Conference Code 2023-03-NLP-MSS 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Correlation dimensions of EEG time series characterizing sleep stages in mice 
Sub Title (in English)  
Keyword(1) Non-Linear Time Series Analysis  
Keyword(2) Correlation Dimension  
Keyword(3) GP Algorithm  
Keyword(4) FNN Algorithm  
Keyword(5) Sleep EEG Data  
Keyword(6) Mouse  
Keyword(7)  
Keyword(8)  
1st Author's Name Kazuki Koyama  
1st Author's Affiliation Rikkyo University (Rikkyo Univ.)
2nd Author's Name Masanori Sakaguchi  
2nd Author's Affiliation University of Tsukuba (Univ. Tsukuba)
3rd Author's Name Takaaki Ohnishi  
3rd Author's Affiliation Rikkyo University (Rikkyo Univ.)
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Speaker Author-1 
Date Time 2023-03-15 16:15:00 
Presentation Time 20 minutes 
Registration for NLP 
Paper # MSS2022-77, NLP2022-122 
Volume (vol) vol.122 
Number (no) no.435(MSS), no.436(NLP) 
Page pp.75-80 
#Pages
Date of Issue 2023-03-08 (MSS, NLP) 


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