{"metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00219604","sets":["6164:6165:6640:11008"]},"path":["11008"],"owner":"44499","recid":"219604","title":["K近傍法によるてんかん発作時脳波識別手法の提案"],"pubdate":{"attribute_name":"公開日","attribute_value":"2022-07-06"},"_buckets":{"deposit":"69684daf-9ac8-4f45-9b7e-a768ee337db5"},"_deposit":{"id":"219604","pid":{"type":"depid","value":"219604","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"K近傍法によるてんかん発作時脳波識別手法の提案","author_link":["572919","572921","572920","572922"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"K近傍法によるてんかん発作時脳波識別手法の提案"}]},"item_type_id":"18","publish_date":"2022-07-06","item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"jpn"}]},"item_18_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"慶應義塾大学大学院理工学研究科"},{"subitem_text_value":"慶應義塾大学大学院理工学研究科"},{"subitem_text_value":"慶應義塾大学大学院理工学研究科"},{"subitem_text_value":"慶應義塾大学大学院理工学研究科"}]},"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/219604/files/IPSJ-DICOMO2022030.pdf","label":"IPSJ-DICOMO2022030.pdf"},"date":[{"dateType":"Available","dateValue":"2024-07-06"}],"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-DICOMO2022030.pdf","filesize":[{"value":"1.7 MB"}],"mimetype":"application/pdf","priceinfo":[{"tax":["include_tax"],"price":"660","billingrole":"5"},{"tax":["include_tax"],"price":"330","billingrole":"6"},{"tax":["include_tax"],"price":"0","billingrole":"44"}],"accessrole":"open_date","version_id":"24b20e5e-0b8d-4d7c-a963-1a0b3bee46c0","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2022 by the Information Processing Society of Japan"}]},"item_18_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"北川, 栞"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"山崎, 雄貴"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"土井, 千章"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"重野, 寛"}],"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_18_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"てんかんとは大脳の神経細胞の過剰な活動によって引き起こされる反服的な発作を生じる脳疾患である．抗てんかん薬を用いることにより，てんかんの発作を抑制できる．てんかんの確定診断及び投薬には患者の脳波を測定し医師が確認することが有効とされる．しかし，専門医や長時間の脳波測定を行える施設の不足による医療現場の負担や，適切な投薬を受け続けることの難しさが課題である．本稿では，医師のてんかん診断の補助を行うことを目的として，機械学習手法を用いて脳波データからてんかん発作時の脳波を識別するモデルを構築する．また，家庭で使用できる簡易的な脳波計を用いた識別を可能とするために識別に必要な脳波計のチャンネル数を削減する方法とその下限を検討する．識別モデルを用いることで脳波データからてんかん発作を F1 score:0.995 で識別できることを確認した．また 15 チャンネルという簡易的な脳波計で有り得るチャンネル数でも性能を大きく損なうことなく発作の識別が可能であることを確認した．","subitem_description_type":"Other"}]},"item_18_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"220","bibliographic_titles":[{"bibliographic_title":"マルチメディア，分散，協調とモバイルシンポジウム2022論文集"}],"bibliographicPageStart":"215","bibliographicIssueDates":{"bibliographicIssueDate":"2022-07-06","bibliographicIssueDateType":"Issued"},"bibliographicVolumeNumber":"2022"}]},"relation_version_is_last":true,"weko_creator_id":"44499"},"id":219604,"updated":"2025-01-19T14:50:12.556877+00:00","links":{},"created":"2025-01-19T01:19:40.457927+00:00"}