Detail publikace

Spektrální a statistická analýza: použití pro studium ischemie v izolovaných králičích srdcích

RONZHINA, M. POTOČŇÁK, T. JANOUŠEK, O. KOLÁŘOVÁ, J. NOVÁKOVÁ, M. PROVAZNÍK, I.

Originální název

Spectral and Higher-Order Statistics Analysis of ECG: Application to Study of Ischemia in Rabbit Isolated Hearts

Český název

Spektrální a statistická analýza: použití pro studium ischemie v izolovaných králičích srdcích

Anglický název

Spectral and Higher-Order Statistics Analysis of ECG: Application to Study of Ischemia in Rabbit Isolated Hearts

Typ

článek ve sborníku

Jazyk

en

Originální abstrakt

The present paper is focused on the study of ECG cross spectral coherence and higher-order cumulants and their ability to classify normal and ischemic cardiac beats. Using these parameters as the input for neural network classifier allows achieving classification error only 4%.

Český abstrakt

The present paper is focused on the study of ECG cross spectral coherence and higher-order cumulants and their ability to classify normal and ischemic cardiac beats. Using these parameters as the input for neural network classifier allows achieving classification error only 4%.

Anglický abstrakt

The present paper is focused on the study of ECG cross spectral coherence and higher-order cumulants and their ability to classify normal and ischemic cardiac beats. Using these parameters as the input for neural network classifier allows achieving classification error only 4%.

Klíčová slova

Rabbits isolated heart, ischemia, ECG classification, cross spectral coherence, higher-order cumulants, neural network

Rok RIV

2012

Vydáno

10.09.2012

ISBN

978-1-4673-2076-4

Kniha

Computing in Cardiology 2012

Strany od

645

Strany do

648

Strany počet

4

BibTex


@inproceedings{BUT93840,
  author="Marina {Ronzhina} and Tomáš {Potočňák} and Oto {Janoušek} and Jana {Kolářová} and Marie {Nováková} and Ivo {Provazník}",
  title="Spectral and Higher-Order Statistics Analysis of ECG: Application to Study of Ischemia in Rabbit Isolated Hearts",
  annote="The present paper is focused on the study of ECG cross spectral coherence and higher-order cumulants and their ability to classify normal and ischemic cardiac beats. Using these parameters as the input for neural network classifier allows achieving classification error only 4%.",
  booktitle="Computing in Cardiology 2012",
  chapter="93840",
  howpublished="electronic, physical medium",
  year="2012",
  month="september",
  pages="645--648",
  type="conference paper"
}