Systolic Blood Pressure Classification

S. Colak, C. Isik

Research output: Contribution to conferencePaperpeer-review

5 Scopus citations

Abstract

To classify systolic, mean and diastolic blood pressure using the oscillometric method heavily depends on the computational algorithms. Generally, the algorithms aim at extracting some parameters such as height, ratios of the pulses at certain pressure levels, which are obtained from the cuff pressure. These parameters can be used to form profiles to attribute to blood pressures. Our algorithms are based on fuzzy sets, whose membership functions are determined by using neural networks. We further employ Gram-Schmidt orthogonal transformation to select appropriate features for classification. The effectiveness of neural network solution to systolic blood pressure classification is the focus of this paper.

Original languageEnglish (US)
Pages627-630
Number of pages4
StatePublished - Sep 24 2003
EventInternational Joint Conference on Neural Networks 2003 - Portland, OR, United States
Duration: Jul 20 2003Jul 24 2003

Other

OtherInternational Joint Conference on Neural Networks 2003
CountryUnited States
CityPortland, OR
Period7/20/037/24/03

Keywords

  • Blood pressure classification
  • Feature selection
  • Fuzzy sets
  • Orthogonal transform

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence

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