{"links":{},"id":180740,"metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00180740","sets":["6504:9168:9183"]},"path":["9183"],"owner":"6748","recid":"180740","title":["深層学習を用いたマウスの睡眠ステージ分析"],"pubdate":{"attribute_name":"公開日","attribute_value":"2017-03-16"},"_buckets":{"deposit":"12bec10b-8b4e-4aad-94dd-eb373c830379"},"_deposit":{"id":"180740","pid":{"type":"depid","value":"180740","revision_id":0},"owners":[6748],"status":"published","created_by":6748},"item_title":"深層学習を用いたマウスの睡眠ステージ分析","author_link":["390722","390723","390720","390721","390719"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"深層学習を用いたマウスの睡眠ステージ分析"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"データとウェブ","subitem_subject_scheme":"Other"}]},"item_type_id":"22","publish_date":"2017-03-16","item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"jpn"}]},"item_22_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"筑波大"},{"subitem_text_value":"筑波大"},{"subitem_text_value":"筑波大"},{"subitem_text_value":"筑波大"},{"subitem_text_value":"筑波大"}]},"item_publisher":{"attribute_name":"出版者","attribute_value_mlt":[{"subitem_publisher":"情報処理学会","subitem_publisher_language":"ja"}]},"publish_status":"0","weko_shared_id":-1,"item_file_price":{"attribute_name":"Billing file","attribute_type":"file","attribute_value_mlt":[{"url":{"url":"https://ipsj.ixsq.nii.ac.jp/record/180740/files/IPSJ-Z79-5K-03.pdf","label":"IPSJ-Z79-5K-03.pdf"},"date":[{"dateType":"Available","dateValue":"2017-05-22"}],"format":"application/pdf","filename":"IPSJ-Z79-5K-03.pdf","filesize":[{"value":"263.2 kB"}],"mimetype":"application/pdf","accessrole":"open_date","version_id":"9f53128b-fbaa-4400-bbf7-a43715fdbbac","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2017 by the Information Processing Society of Japan"}]},"item_22_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"山部, 剛士"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"北川, 博之"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"塩川, 浩昭"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"柳沢, 正史"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"佐藤, 牧人"}],"nameIdentifiers":[{}]}]},"item_resource_type":{"attribute_name":"資源タイプ","attribute_value_mlt":[{"resourceuri":"http://purl.org/coar/resource_type/c_5794","resourcetype":"conference paper"}]},"item_22_source_id_9":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AN00349328","subitem_source_identifier_type":"NCID"}]},"item_22_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"マウスの睡眠はレム・ノンレム・覚醒の3つの睡眠ステージに分類することができ，睡眠ステージの情報は睡眠の研究を行う上で非常に重要な情報となっている．しかし，現在この睡眠ステージの判定は熟練した専門家が脳波・筋電のデータをもとに目視で判定を行っており，多くの時間や労力を要している．そこで，本研究では近年画像認識や音声認識などの分野で成果をあげているRecurrent Neural Network (RNN)を用いた高精度な睡眠ステージ判定手法を提案する．提案手法では脳波・筋電データをエポックと呼ばれる一定区間に分割し，エポック毎に判定を行う．RNNはエポックの脳波・筋電データの周波数スペクトルとエポック間の時系列性に基づいて各睡眠ステージの推定値を求める．提案手法によって覚醒・ノンレムについて95%以上，レムについて約90%の判定精度を達成した．","subitem_description_type":"Other"}]},"item_22_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"498","bibliographic_titles":[{"bibliographic_title":"第79回全国大会講演論文集"}],"bibliographicPageStart":"497","bibliographicIssueDates":{"bibliographicIssueDate":"2017-03-16","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"1","bibliographicVolumeNumber":"2017"}]},"relation_version_is_last":true,"weko_creator_id":"6748"},"created":"2025-01-19T00:48:33.487708+00:00","updated":"2025-01-20T04:47:14.268360+00:00"}