Detail publikace

Heart beat classification

POTOČŇÁK, T. RONZHINA, M.

Originální název

Heart beat classification

Český název

Heart beat classification

Anglický název

Heart beat classification

Typ

článek ve sborníku

Jazyk

sk

Originální abstrakt

The aim of this work was to develop the method for classification of ECG beats into two classes, namely ischemic and non-ischemic beats. Heart beats (QRS-T cycles) from animal orthogonal ECGs were preprocessed and used as input signal. Spectral features vectors (values of cross spectral coherency) were derived from the beats and the beats were classified using feedforward multilayer neural network designed in Matlab. Classification performance reached the value approx. 99%. Presented results can be suitable in future studies aimed to automatic classification of ECG.

Český abstrakt

The aim of this work was to develop the method for classification of ECG beats into two classes, namely ischemic and non-ischemic beats. Heart beats (QRS-T cycles) from animal orthogonal ECGs were preprocessed and used as input signal. Spectral features vectors (values of cross spectral coherency) were derived from the beats and the beats were classified using feedforward multilayer neural network designed in Matlab. Classification performance reached the value approx. 99%. Presented results can be suitable in future studies aimed to automatic classification of ECG.

Anglický abstrakt

The aim of this work was to develop the method for classification of ECG beats into two classes, namely ischemic and non-ischemic beats. Heart beats (QRS-T cycles) from animal orthogonal ECGs were preprocessed and used as input signal. Spectral features vectors (values of cross spectral coherency) were derived from the beats and the beats were classified using feedforward multilayer neural network designed in Matlab. Classification performance reached the value approx. 99%. Presented results can be suitable in future studies aimed to automatic classification of ECG.

Klíčová slova

Heart beat classification, cardiac ischemia, cross spectral coherency analysis, artificial neural network

Rok RIV

2013

Vydáno

25.04.2013

ISBN

978-80-214-4694-6

Kniha

Proceedings of the 19th Conference STUDENT EEICT 2013 Volume 2

Číslo edice

1

Strany od

182

Strany do

184

Strany počet

3

BibTex


@inproceedings{BUT99449,
  author="Tomáš {Potočňák} and Marina {Ronzhina}",
  title="Heart beat classification",
  annote="The aim of this work was to develop the method for classification of ECG beats into two classes, namely ischemic and non-ischemic beats. Heart beats (QRS-T cycles) from animal orthogonal ECGs were preprocessed and used as input signal. Spectral features vectors (values of cross spectral coherency) were derived from the beats and the beats were classified using feedforward multilayer neural network designed in Matlab. Classification performance reached the value approx. 99%. Presented results can be suitable in future studies aimed to automatic classification of ECG.",
  booktitle="Proceedings of the 19th Conference STUDENT EEICT 2013 Volume 2",
  chapter="99449",
  howpublished="print",
  year="2013",
  month="april",
  pages="182--184",
  type="conference paper"
}