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Please use this identifier to cite or link to this item: http://elib.bsu.by/handle/123456789/114020
Title: Analysis of 24-hour ambulatory blood pressure monitoring data using support vector machine
Authors: Voitikova, M. V.
Khursa, R. V.
Keywords: ЭБ БГУ::ТЕХНИЧЕСКИЕ И ПРИКЛАДНЫЕ НАУКИ. ОТРАСЛИ ЭКОНОМИКИ::Медицина и здравоохранение
ЭБ БГУ::ТЕХНИЧЕСКИЕ И ПРИКЛАДНЫЕ НАУКИ. ОТРАСЛИ ЭКОНОМИКИ::Автоматика. Вычислительная техника
Issue Date: 2014
Publisher: Minsk : Education and Upbringing
Citation: Nonlinear Phenomena in Complex Systems. - 2014. - Vol. 17, N 1. - P. 50-56
Abstract: This 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%.
URI: http://elib.bsu.by/handle/123456789/114020
ISSN: 1561-4085
Appears in Collections:2014. Volume 17. Number 1

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