Comparison of Machine Learning Techniques for Fetal Heart Rate Classification

dc.contributor.author	Comert, Z.
dc.contributor.author	Kocamaz, A. F.
dc.date.accessioned	16/12/21 12:07
dc.date.available	16/12/21 12:07
dc.date.issued	2017
dc.identifier.issn	0587-4246
dc.identifier.uri	http://dspace.beu.edu.tr:8080/xmlui/handle/20.500.12643/10347
dc.identifier.uri	https://doi.org/10.12693/APhysPolA.132.451
dc.description.abstract	Cardiotocography is a monitoring technique providing important and vital information on fetal status during antepartum and intrapartum periods. The advances in modern obstetric practice allowed many robust and reliable machine learning techniques to be ut
dc.language.iso	English
dc.publisher	Polısh Acad Scıences Inst Physıcs
dc.relation.ispartof	3rd International Conference on Computational and Experimental Science and Engineering (ICCESEN)
dc.rights	gold
dc.source	Acta Physıca Polonıca A
dc.title	Comparison of Machine Learning Techniques for Fetal Heart Rate Classification
dc.type	Article; Proceedings Paper
dc.identifier.issue	3
dc.identifier.startpage	451
dc.identifier.endpage	454
dc.identifier.doi	10.12693/APhysPolA.132.451
dc.identifier.wos	WOS:000412881200013
dc.identifier.volume	132

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