{"id":217997,"updated":"2025-01-19T15:18:40.275465+00:00","links":{},"created":"2025-01-19T01:18:25.137709+00:00","metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00217997","sets":["1164:3865:10834:10912"]},"path":["10912"],"owner":"44499","recid":"217997","title":["Unsupervised Learning-based Non-invasive Fetal ECG Signal Quality Assessment"],"pubdate":{"attribute_name":"公開日","attribute_value":"2022-05-19"},"_buckets":{"deposit":"1f7a634d-e25f-4aef-bf3e-a426bad833e0"},"_deposit":{"id":"217997","pid":{"type":"depid","value":"217997","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"Unsupervised Learning-based Non-invasive Fetal ECG Signal Quality Assessment","author_link":["565898","565892","565896","565895","565901","565893","565897","565899","565900","565894"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"Unsupervised Learning-based Non-invasive Fetal ECG Signal Quality Assessment"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"医療・健康・生活支援","subitem_subject_scheme":"Other"}]},"item_type_id":"4","publish_date":"2022-05-19","item_4_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"Department of Information and Computer Science Keio University"},{"subitem_text_value":"Department of Information and Computer Science Keio University"},{"subitem_text_value":"Department of Information and Computer Science Keio University"},{"subitem_text_value":"Research & Development Group Technical Department Atom Medical Co. Ltd,"},{"subitem_text_value":"Research & Development Group Technical Department Atom Medical Co. Ltd,"}]},"item_4_text_4":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_text_value":"Department of Information and Computer Science Keio University","subitem_text_language":"en"},{"subitem_text_value":"Department of Information and Computer Science Keio University","subitem_text_language":"en"},{"subitem_text_value":"Department of Information and Computer Science Keio University","subitem_text_language":"en"},{"subitem_text_value":"Research & Development Group Technical Department Atom Medical Co. Ltd,","subitem_text_language":"en"},{"subitem_text_value":"Research & Development Group Technical Department Atom Medical Co. Ltd,","subitem_text_language":"en"}]},"item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"eng"}]},"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/217997/files/IPSJ-MBL22103010.pdf","label":"IPSJ-MBL22103010.pdf"},"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-MBL22103010.pdf","filesize":[{"value":"1.4 MB"}],"mimetype":"application/pdf","priceinfo":[{"tax":["include_tax"],"price":"0","billingrole":"35"},{"tax":["include_tax"],"price":"0","billingrole":"44"}],"accessrole":"open_login","version_id":"64a452ca-a1b5-47f0-ad64-f46f598aab32","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2022 by the Institute of Electronics, Information and Communication Engineers This SIG report is only available to those in membership of the SIG."}]},"item_4_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Xintong, Shi"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Kohei, Yamamoto"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Tomoaki, Ohtsuki"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Yutaka, Matsui"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Kazunari, Owada"}],"nameIdentifiers":[{}]}]},"item_4_creator_6":{"attribute_name":"著者名(英)","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Xintong, Shi","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Kohei, Yamamoto","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Tomoaki, Ohtsuki","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Yutaka, Matsui","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Kazunari, Owada","creatorNameLang":"en"}],"nameIdentifiers":[{}]}]},"item_4_source_id_9":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AA11851388","subitem_source_identifier_type":"NCID"}]},"item_4_textarea_12":{"attribute_name":"Notice","attribute_value_mlt":[{"subitem_textarea_value":"SIG Technical Reports are nonrefereed and hence may later appear in any journals, conferences, symposia, etc."}]},"item_resource_type":{"attribute_name":"資源タイプ","attribute_value_mlt":[{"resourceuri":"http://purl.org/coar/resource_type/c_18gh","resourcetype":"technical report"}]},"item_4_source_id_11":{"attribute_name":"ISSN","attribute_value_mlt":[{"subitem_source_identifier":"2188-8817","subitem_source_identifier_type":"ISSN"}]},"item_4_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"For fetal heart rate (FHR) monitoring, the non-invasive fetal electrocardiogram (FECG) obtained from abdomen surface electrodes has been widely employed. The accuracy of FECG-based FHR estimation, on the other hand, is highly dependent on the quality of the FECG signals, which can be influenced by a variety of interference sources, including maternal cardiac activities and fetal movements. Thus, FECG signal quality assessment (SQA) is a critical task in improving FHR estimation accuracy by eliminating or interpolating FHRs estimated from low-quality FECG signals. Various SQA approaches based on supervised learning have been proposed in recent studies. These approaches are capable of accurate SQA, however, they require big datasets with annotations. Nevertheless, the annotated datasets for the FECG SQA are extremely limited. In this research, to deal with this problem, we introduce an unsupervised learning-based SQA approach for identifying high and low-quality FECG signal segments. Some features associated with signal quality are extracted, including four entropy-based features, three statistical features, and four ECG signal quality indices (SQIs). In addition to these features, we introduce an autoencoder (AE)-based feature, which is based on reconstruction error of AE that reconstructs spectrograms obtained from FECG signal segments. The collected features are then input into the self-organizing map (SOM) to classify the high and low-quality FECG segments. The experimental results indicated that our approach classified high and low-quality signals with a 98% accuracy.","subitem_description_type":"Other"}]},"item_4_description_8":{"attribute_name":"論文抄録(英)","attribute_value_mlt":[{"subitem_description":"For fetal heart rate (FHR) monitoring, the non-invasive fetal electrocardiogram (FECG) obtained from abdomen surface electrodes has been widely employed. The accuracy of FECG-based FHR estimation, on the other hand, is highly dependent on the quality of the FECG signals, which can be influenced by a variety of interference sources, including maternal cardiac activities and fetal movements. Thus, FECG signal quality assessment (SQA) is a critical task in improving FHR estimation accuracy by eliminating or interpolating FHRs estimated from low-quality FECG signals. Various SQA approaches based on supervised learning have been proposed in recent studies. These approaches are capable of accurate SQA, however, they require big datasets with annotations. Nevertheless, the annotated datasets for the FECG SQA are extremely limited. In this research, to deal with this problem, we introduce an unsupervised learning-based SQA approach for identifying high and low-quality FECG signal segments. Some features associated with signal quality are extracted, including four entropy-based features, three statistical features, and four ECG signal quality indices (SQIs). In addition to these features, we introduce an autoencoder (AE)-based feature, which is based on reconstruction error of AE that reconstructs spectrograms obtained from FECG signal segments. The collected features are then input into the self-organizing map (SOM) to classify the high and low-quality FECG segments. The experimental results indicated that our approach classified high and low-quality signals with a 98% accuracy.","subitem_description_type":"Other"}]},"item_4_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"5","bibliographic_titles":[{"bibliographic_title":"研究報告モバイルコンピューティングと新社会システム（MBL）"}],"bibliographicPageStart":"1","bibliographicIssueDates":{"bibliographicIssueDate":"2022-05-19","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"10","bibliographicVolumeNumber":"2022-MBL-103"}]},"relation_version_is_last":true,"weko_creator_id":"44499"}}