| Presentation | 2022-03-02 [Poster Presentation] Epileptic Seizure Detection Using Active Learning with Riemannian Manifold Toshiki Orihara, Toshihisa Tanaka, |
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| PDF Download Page |  PDF download Page Link |
| Abstract(in Japanese) | (See Japanese page) |
| Abstract(in English) | In order to realize machine learning for diagnosis, it is necessary to solve the problem that the training model is not always effective for unknown data (domain adaptation problem) because the distribution of data is different for each patient. In this paper, we apply the unsupervised domain adaptation method by translation on a riemannian manifold to seizure detection for EEG of epilepsy patients. We propose a mechanism to actively train the model by translating both the source and target data to the centre point of the source domain, which is close to the riemannian distance from the target domain. We conducted inter-patient validation using a public dataset including seizure EEG, and confirmed that the proposed method can detect seizure EEG with high AUC and correct answer rate. It is suggested that the proposed method can be applied not only to seizure detection for EEG but also to the identification of EEG with domain shift in general. |
| Keyword(in Japanese) | (See Japanese page) |
| Keyword(in English) | Machine learning / Electroencephalogram / Epilepsy / Domain adaptation / Riemannian manifold |
| Paper # | EA2021-96,SIP2021-123,SP2021-81 |
| Date of Issue | 2022-02-22 (EA, SIP, SP) |
| Conference Information | |
| Committee | EA / SIP / SP / IPSJ-SLP |
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| Conference Date | 2022/3/1(2days) |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | |
| Chair | Yoshinobu Kajikawa(Kansai Univ.) / Yukihiro Bandou(NTT) / Norihide Kitaoka(Toyohashi Univ. of Tec) / 北岡 教英(豊橋技科大) |
| Vice Chair | Kenichi Furuya(Oita Univ.) / Shoichi Koyama(Univ. of Tokyo) / Toshihisa Tanaka(Tokyo Univ. Agri.&Tech.) / Takayuki Nakachi(Ryukyu Univ.) |
| Secretary | Kenichi Furuya(NTT) / Shoichi Koyama(RitsumeikanUniv.) / Toshihisa Tanaka(Xiaomi) / Takayuki Nakachi(Takushoku Univ.) / (Tokyo Univ. Agri.&Tech.) / (Univ. of Tokyo) |
| Assistant | Yukou Wakabayashi(Tokyo Metropolitan Univ.) / Tatsuya Komatsu(LINE) / Taichi Yoshida(UEC) / Seisuke Kyochi(Univ. of Kitakyushu) / Toru Nakashika(Univ. of Electro-Comm.) / Ryo Masumura(NTT) |
| Paper Information | |
| Registration To | Technical Committee on Engineering Acoustics / Technical Committee on Signal Processing / Technical Committee on Speech / Special Interest Group on Spoken Language Processing |
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| Language | JPN |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | [Poster Presentation] Epileptic Seizure Detection Using Active Learning with Riemannian Manifold |
| Sub Title (in English) | |
| Keyword(1) | Machine learning |
| Keyword(2) | Electroencephalogram |
| Keyword(3) | Epilepsy |
| Keyword(4) | Domain adaptation |
| Keyword(5) | Riemannian manifold |
| 1st Author's Name | Toshiki Orihara |
| 1st Author's Affiliation | Tokyo University of Agriculture and Technology(TUAT) |
| 2nd Author's Name | Toshihisa Tanaka |
| 2nd Author's Affiliation | Tokyo University of Agriculture and Technology(TUAT) |
| Date | 2022-03-02 |
| Paper # | EA2021-96,SIP2021-123,SP2021-81 |
| Volume (vol) | vol.121 |
| Number (no) | EA-383,SIP-384,SP-385 |
| Page | pp.pp.201-206(EA), pp.201-206(SIP), pp.201-206(SP), |
| #Pages | 6 |
| Date of Issue | 2022-02-22 (EA, SIP, SP) |