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<oai_dc:dc xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:dcterms="http://purl.org/dc/terms/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd http://dublincore.org/schemas/xmls/qdc/dcterms.xsd"><dc:language>eng</dc:language><dc:creator>Hu, Yating</dc:creator><dc:creator>Feng, Tengfei</dc:creator><dc:creator>Wang, Miao</dc:creator><dc:creator>Liu, Chengyu</dc:creator><dc:creator>Tang, Hong</dc:creator><dc:title>Detection of Paroxysmal Atrial Fibrillation from Dynamic ECG Recordings Based on a Deep Learning Model</dc:title><dc:source>Journal of Personalized Medicine 13(5), 820 (2023). doi:10.3390/jpm13050820 special issue: &quot;Physiological Signal Analysis Methods in Healthcare / Special Issue Editors: Prof. Dr. Hsien-Tsai Wu, Guest Editor; Prof. Dr. Cheuk-Kwan Sun, Guest Editor; Prof. Dr. Cheuk-Kwan Sun, Guest Editor&quot;</dc:source><dc:type>info:eu-repo/semantics/article</dc:type><dc:type>info:eu-repo/semantics/publishedVersion</dc:type><dc:publisher>MDPI</dc:publisher><dc:date>2023</dc:date><dc:rights>info:eu-repo/semantics/openAccess</dc:rights><dc:coverage>DE</dc:coverage><dc:identifier>https://publications.rwth-aachen.de/record/982876</dc:identifier><dc:identifier>https://publications.rwth-aachen.de/search?p=id:%22RWTH-CONV-252939%22</dc:identifier><dc:audience>Researchers</dc:audience><dc:relation>info:eu-repo/semantics/altIdentifier/issn/2075-4426</dc:relation><dc:relation>info:eu-repo/semantics/altIdentifier/doi/10.18154/RWTH-CONV-252939</dc:relation><dc:relation>info:eu-repo/semantics/altIdentifier/doi/10.3390/jpm13050820</dc:relation><dc:relation>info:eu-repo/semantics/altIdentifier/pmid/pmid:37240990</dc:relation><dc:relation>info:eu-repo/semantics/altIdentifier/wos/WOS:001020130700001</dc:relation></oai_dc:dc>

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