Publication result detail

ECG classification using neural networks and McSharry model

KIČMEROVÁ, D.; PROVAZNÍK, I.

Original Title

ECG classification using neural networks and McSharry model

English Title

ECG classification using neural networks and McSharry model

Type

Paper in proceedings (conference paper)

Original Abstract

The presented work is focused on modelling of arrhythmias using McSharry model followed by classification using an artificial neural network. The proposed method uses preprocessing of signals with Linear Approximation Distance Thresholding method and Line Segment Clustering method for establishing of initial parameters of McSharry model. The ECG data was taken from standard MIT/BIH arrhythmia database. Multilayer perceptron was used with classification accuracy of 90.1% for distinguishing of premature ventricular contraction and normal beat.

English abstract

The presented work is focused on modelling of arrhythmias using McSharry model followed by classification using an artificial neural network. The proposed method uses preprocessing of signals with Linear Approximation Distance Thresholding method and Line Segment Clustering method for establishing of initial parameters of McSharry model. The ECG data was taken from standard MIT/BIH arrhythmia database. Multilayer perceptron was used with classification accuracy of 90.1% for distinguishing of premature ventricular contraction and normal beat.

Keywords

ECG, McSharry model, neural networks

Key words in English

ECG, McSharry model, neural networks

Authors

KIČMEROVÁ, D.; PROVAZNÍK, I.

Released

22.08.2007

Publisher

IEEE

Location

Brno

ISBN

978-80-214-3409-7

Book

Proceedings of 5th IEEE Workshop Zvule

Pages from

1

Pages to

1

Pages count

1

BibTex

@inproceedings{BUT22837,
  author="Dina {Kičmerová} and Valentýna {Provazník}",
  title="ECG classification using neural networks and McSharry model",
  booktitle="Proceedings of 5th IEEE Workshop Zvule",
  year="2007",
  pages="1--1",
  publisher="IEEE",
  address="Brno",
  isbn="978-80-214-3409-7"
}