ECG Signal Classification using GAME Neural Network and its comparison to other classifiers

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F08%3A03145833" target="_blank" >RIV/68407700:21230/08:03145833 - isvavai.cz</a>

Result on the web

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DOI - Digital Object Identifier

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Result language

angličtina

Original language name

ECG Signal Classification using GAME Neural Network and its comparison to other classifiers

Original language description

Long term Holter monitoring is widely applied to patients with heart diseases. Many of those diseases are not constantly present in the ECG signal but occurs from time to time. To detect these infrequent problems the Holter long time ECG recording is recorded and analysed. There are many methods for automatic detection of irregularities in the ECG signal. In this paper we will comapare the Support Vector Machine (SVM), J48 decision tree (J48), RBF artificial neural network (RBF), Simple logistic function and our novel GAME neural network for detection of the Premature Ventricular Contractions. We will compare and discuss classification performance of mentioned methods. There are also very many features which describes the ECG signal therefore we will try to identify features important for correct classification and examine how the accuracy is affected with only selected features in training set.

Czech name

ECG Signal Classification using GAME Neural Network and its comparison to other classifiers

Czech description

Long term Holter monitoring is widely applied to patients with heart diseases. Many of those diseases are not constantly present in the ECG signal but occurs from time to time. To detect these infrequent problems the Holter long time ECG recording is recorded and analysed. There are many methods for automatic detection of irregularities in the ECG signal. In this paper we will comapare the Support Vector Machine (SVM), J48 decision tree (J48), RBF artificial neural network (RBF), Simple logistic function and our novel GAME neural network for detection of the Premature Ventricular Contractions. We will compare and discuss classification performance of mentioned methods. There are also very many features which describes the ECG signal therefore we will try to identify features important for correct classification and examine how the accuracy is affected with only selected features in training set.

Type

D - Article in proceedings

CEP classification

IN - Informatics

OECD FORD branch

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Project

<a href="/en/project/KJB201210701" target="_blank" >KJB201210701: Automated Knowledge Extraction</a><br>

Continuities

Z - Vyzkumny zamer (s odkazem do CEZ)

Publication year

2008

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection

Artificial Neural Networks - ICANN 2008, PT I

ISBN

978-3-540-87535-2

ISSN

0302-9743

e-ISSN

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Number of pages

10

Pages from-to

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Publisher name

Springer

Place of publication

Heidelberg

Event location

Prague

Event date

Sep 3, 2008

Type of event by nationality

EUR - Evropská akce

UT code for WoS article

000259566200079