{"created":"2023-06-20T14:06:56.307586+00:00","id":407,"links":{},"metadata":{"_buckets":{"deposit":"c2312b3e-81cb-4ee5-80d6-9b122a20d229"},"_deposit":{"created_by":4,"id":"407","owners":[4],"pid":{"revision_id":0,"type":"depid","value":"407"},"status":"published"},"_oai":{"id":"oai:maebashi-it.repo.nii.ac.jp:00000407","sets":["8:35"]},"author_link":["1238","1237"],"item_3_biblio_info_7":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicIssueDates":{"bibliographicIssueDate":"2020-03-31","bibliographicIssueDateType":"Issued"},"bibliographic_titles":[{"bibliographic_title":"前橋工科大学研究紀要"}]}]},"item_3_description_5":{"attribute_name":"抄録","attribute_value_mlt":[{"subitem_description":"Electroencephalogram (EEG) measurement, being an appropriate approach to\nunderstanding the underlying mechanisms of the major depressive disorder (MDD), is\nused to discriminate between depressive and normal control. With the advancement of\ndeep learning methods, many studies have designed deep learning models to improve the\nclassification accuracy of depression discrimination. However, few of them have focused\non designing a convolutional filter to learn features according to EEG activity\ncharacteristics. I n this study, a novel convolutional neural network named\nHybridEEGNet that is composed of two parallel lines is proposed to learn the\nsynchronous and regional EEG features, and further differentiate normal controls from\nmedicated and unmedicated MDD patien ts. A ten fold cross validation method is used to\ntrain and test the model. The results show that HybridEEGNet achieves a sensitivity of\n68.78%, a specificity of 84.45%, and an accuracy of 79.08% in three category\nclassification.","subitem_description_type":"Abstract"}]},"item_3_source_id_10":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AA11225201","subitem_source_identifier_type":"NCID"}]},"item_3_source_id_8":{"attribute_name":"ISSN","attribute_value_mlt":[{"subitem_source_identifier":"1343-8867","subitem_source_identifier_type":"ISSN"}]},"item_creator":{"attribute_name":"著者","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"万, 志江"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"鍾, 寧"}],"nameIdentifiers":[{}]}]},"item_files":{"attribute_name":"ファイル情報","attribute_type":"file","attribute_value_mlt":[{"accessrole":"open_date","date":[{"dateType":"Available","dateValue":"2022-03-22"}],"displaytype":"detail","filename":"23_03.pdf","filesize":[{"value":"529.6 kB"}],"format":"application/pdf","licensetype":"license_note","mimetype":"application/pdf","url":{"label":"23_03","url":"https://maebashi-it.repo.nii.ac.jp/record/407/files/23_03.pdf"},"version_id":"bf31a236-235e-4e7a-bd7e-0fcb2b611dbd"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"畳み込みニューラルネットワーク"},{"subitem_subject":"うつ状態判別"},{"subitem_subject":"脳波"},{"subitem_subject":"convolutional neural network","subitem_subject_language":"en"},{"subitem_subject":"depression discrimination","subitem_subject_language":"en"},{"subitem_subject":"EEG","subitem_subject_language":"en"}]},"item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"jpn"}]},"item_title":"畳み込みニューラルネットワーク に基づくうつ病の弁別","item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"畳み込みニューラルネットワーク に基づくうつ病の弁別"},{"subitem_title":"A Convolutional Neural Network for Depression Discrimination","subitem_title_language":"en"}]},"item_type_id":"3","owner":"4","path":["35"],"pubdate":{"attribute_name":"公開日","attribute_value":"2022-03-22"},"publish_date":"2022-03-22","publish_status":"0","recid":"407","relation_version_is_last":true,"title":["畳み込みニューラルネットワーク に基づくうつ病の弁別"],"weko_creator_id":"4","weko_shared_id":-1},"updated":"2023-06-20T14:10:24.857510+00:00"}