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Classification of health deterioration by geometry invariants

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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


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