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dc.contributor.authorVoitikova, M. V.-
dc.contributor.authorKhursa, R. V.-
dc.date.accessioned2015-05-20T08:32:41Z-
dc.date.available2015-05-20T08:32:41Z-
dc.date.issued2014-
dc.identifier.citationNonlinear Phenomena in Complex Systems. - 2014. - Vol. 17, N 1. - P. 50-56ru
dc.identifier.issn1561-4085-
dc.identifier.urihttp://elib.bsu.by/handle/123456789/114020-
dc.description.abstractThis paper presents an effective hemodynamic classification algorithm for blood pressure (BP) monitoring data. The proposed approach takes into account two aspects of the hemodynamic states detection, namely the linear regression modeling of BP parameters and the classification block on the base of Data Mining algorithm called Support Vector Machine (SV M). At first, 4 features are extracted from the BP signals and then these features are reduced to only 2, finally, the SV M-classifier is used to classify the hemodynamic states. The proposed classification method is applied to clinical database. Thus 9 types of the hemodynamic states, including latent hypertension and high-risk hypertension, can be discriminated by SV M-classifier with the accuracy of 96%.ru
dc.language.isoenru
dc.publisherMinsk : Education and Upbringingru
dc.subjectЭБ БГУ::ТЕХНИЧЕСКИЕ И ПРИКЛАДНЫЕ НАУКИ. ОТРАСЛИ ЭКОНОМИКИ::Медицина и здравоохранениеru
dc.subjectЭБ БГУ::ТЕХНИЧЕСКИЕ И ПРИКЛАДНЫЕ НАУКИ. ОТРАСЛИ ЭКОНОМИКИ::Автоматика. Вычислительная техника-
dc.titleAnalysis of 24-hour ambulatory blood pressure monitoring data using support vector machineru
dc.typeArticleru
Appears in Collections:2014. Volume 17. Number 1

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