Classification of health deterioration by geometry invariants

Cimr, Dalibor; Bušovský, Damián; Fujita, Hamido; Studnička, Filip; Cimler, Richard; Hayashi, Toshitaka

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dc.rights.license	CC BY	eng
dc.contributor.author	Cimr, Dalibor	cze
dc.contributor.author	Bušovský, Damián	cze
dc.contributor.author	Fujita, Hamido	cze
dc.contributor.author	Studnička, Filip	cze
dc.contributor.author	Cimler, Richard	cze
dc.contributor.author	Hayashi, Toshitaka	cze
dc.date.accessioned	2025-12-05T12:49:16Z
dc.date.available	2025-12-05T12:49:16Z
dc.date.issued	2023	eng
dc.identifier.issn	0169-2607	eng
dc.identifier.uri	http://hdl.handle.net/20.500.12603/1812
dc.description.abstract	Prediction of patient deterioration is essential in medical care, and its automation may reduce the risk of patient death. The precise monitoring of a patient’s medical state requires devices placed on the body, which may cause discomfort. Our approach is based on the processing of long-term ballistocardiography data, which were measured using a sensory pad placed under the patient’s mattress.	eng
dc.format	p. "Article Number: 107623"	eng
dc.language.iso	eng	eng
dc.publisher	Elsevier	eng
dc.relation.ispartof	Computer Methods and Programs in Biomedicine, volume 239, issue: September	eng
dc.subject	Deterioration detection	eng
dc.subject	Ballistocardiography	eng
dc.subject	Cartan curvature	eng
dc.subject	Convolutional neural network	eng
dc.title	Classification of health deterioration by geometry invariants	eng
dc.type	article	eng
dc.identifier.obd	43880116	eng
dc.identifier.doi	10.1016/j.cmpb.2023.107623	eng
dc.publicationstatus	postprint	eng
dc.peerreviewed	yes	eng
dc.source.url	https://www.sciencedirect.com/science/article/pii/S0169260723002882	cze
dc.relation.publisherversion	https://www.sciencedirect.com/science/article/pii/S0169260723002882	eng
dc.rights.access	Open Access	eng