{"metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00214992","sets":["6504:10735:10808"]},"path":["10808"],"owner":"44499","recid":"214992","title":["リカレントニューラルネットワークによる無拘束肺音分類手法の提案"],"pubdate":{"attribute_name":"公開日","attribute_value":"2021-03-04"},"_buckets":{"deposit":"4bee629c-07a8-4d5b-8ae9-4d011684bfbc"},"_deposit":{"id":"214992","pid":{"type":"depid","value":"214992","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"リカレントニューラルネットワークによる無拘束肺音分類手法の提案","author_link":["553307","553305","553309","553308","553306"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"リカレントニューラルネットワークによる無拘束肺音分類手法の提案"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"人工知能と認知科学","subitem_subject_scheme":"Other"}]},"item_type_id":"22","publish_date":"2021-03-04","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":"青学大"},{"subitem_text_value":"青学大"},{"subitem_text_value":"青学大"},{"subitem_text_value":"国際基督教大"},{"subitem_text_value":"青学大"}]},"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/214992/files/IPSJ-Z83-4Q-06.pdf","label":"IPSJ-Z83-4Q-06.pdf"},"date":[{"dateType":"Available","dateValue":"2021-12-28"}],"format":"application/pdf","filename":"IPSJ-Z83-4Q-06.pdf","filesize":[{"value":"398.6 kB"}],"mimetype":"application/pdf","accessrole":"open_date","version_id":"9c449f27-ca67-483a-8f7c-aa7d6063853a","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2021 by the Information Processing Society of Japan"}]},"item_22_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"JIAHUI, SHAO"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"西尾, 啓汰"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"浜田, 百合"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"鏑木, 崇史"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"栗原, 陽介"}],"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":"肺がんや新型コロナウィルスなどの肺疾患においては、在宅環境で日々の肺音をモニタリングし異常を自動的に検出できると早期発見、早期治療につながる。しかし、肺音は聴診器により診察する必要があるため、在宅環境で日々使用することは難しい。そこで、本研究では高感度圧力センサを用いて就寝中のヒトの肺音を無拘束で計測し、異常音の特性に基づき肺音の種類を分類する手法を提案する。計測した肺音にたいしリカレントニューラルネットワークを適用することで肺音の種類を分類する。検証実験では肺音シミュレーターを用いて、x種類の肺音をベッド上で発生させ圧力センサで計測し、隠れマルコフモデルを適用した場合と分類の精度を比較する。","subitem_description_type":"Other"}]},"item_22_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"384","bibliographic_titles":[{"bibliographic_title":"第83回全国大会講演論文集"}],"bibliographicPageStart":"383","bibliographicIssueDates":{"bibliographicIssueDate":"2021-03-04","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"1","bibliographicVolumeNumber":"2021"}]},"relation_version_is_last":true,"weko_creator_id":"44499"},"id":214992,"updated":"2025-01-19T16:22:17.756422+00:00","links":{},"created":"2025-01-19T01:15:46.812624+00:00"}