| Presentation | 2023-03-15 Correlation dimensions of EEG time series characterizing sleep stages in mice Kazuki Koyama, Masanori Sakaguchi, Takaaki Ohnishi, |
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| PDF Download Page |  PDF download Page Link |
| Abstract(in Japanese) | (See Japanese page) |
| Abstract(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 numberof data was too small to estimate the correlation dimension. |
| Keyword(in Japanese) | (See Japanese page) |
| Keyword(in English) | Non-Linear Time Series Analysis / Correlation Dimension / GP Algorithm / FNN Algorithm / Sleep EEG Data / Mouse |
| Paper # | MSS2022-77,NLP2022-122 |
| Date of Issue | 2023-03-08 (MSS, NLP) |
| Conference Information | |
| Committee | NLP / MSS |
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| Conference Date | 2023/3/15(3days) |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | |
| Chair | Akio Tsuneda(Kumamoto Univ.) / Atsuo Ozaki(Osaka Inst. of Tech.) |
| Vice Chair | Hiroyuki Torikai(Hosei Univ.) / Shingo Yamaguchi(Yamaguchi Univ.) |
| Secretary | Hiroyuki Torikai(Sojo Univ.) / Shingo Yamaguchi(Gifu Univ.) |
| Assistant | Yuichi Yokoi(Nagasaki Univ.) / Yoshikazu Yamanaka(Utsunomiya Univ.) / Masato Shirai(Shimane Univ.) |
| Paper Information | |
| Registration To | Technical Committee on Nonlinear Problems / Technical Committee on Mathematical Systems Science and its Applications |
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| Language | JPN |
| 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 |
| 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.) |
| Date | 2023-03-15 |
| Paper # | MSS2022-77,NLP2022-122 |
| Volume (vol) | vol.122 |
| Number (no) | MSS-435,NLP-436 |
| Page | pp.pp.75-80(MSS), pp.75-80(NLP), |
| #Pages | 6 |
| Date of Issue | 2023-03-08 (MSS, NLP) |