Volume 1 Number 3 (Sep. 2012)
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IJCCE 2012 Vol.1(3): 287-289 ISSN: 2010-3743
DOI: 10.7763/IJCCE.2012.V1.74

Improving EASI ECG Method Using Various Machine Learning and Regression Techniques to Obtain New EASI ECG Model

Wojciech Oleksy, Ewaryst Tkacz, and Zbigniew Budzianowski

Abstract—Main idea of this study was to increase efficiency of the EASI ECG method introduced by Dover in 1988 using various regression techniques. EASI was proven to have high correlation with standard 12 lead ECG. Apart from that it is less susceptible to artefacts, increase mobility of patients and is easier to use because of smaller number of electrodes. Multilayer Perceptron (Artificial Neural Network), Linear Regression, Pace Regression and Bagging Predictors methods were used to improve the quality of the 12-leadelectrocardiogram derived from four (EASI) electrodes.

Index Terms—EASI, ECG, artificial neural network, linear regression, pace regression, bagging predictors.

The authors are with the Institute of Informatics, Faculty of Automatic Control, Electronics and Computer Science at Silesian University of Technology in Gliwice, Poland (e-mail: wojciech.oleksy@polsl.pl).

Cite: Wojciech Oleksy, Ewaryst Tkacz, and Zbigniew Budzianowski, "Improving EASI ECG Method Using Various Machine Learning and Regression Techniques to Obtain New EASI ECG Model," International Journal of Computer and Communication Engineering vol. 1, no. 3, pp. 287-289 , 2012.

General Information

ISSN: 2010-3743
Frequency: Quarterly
Editor-in-Chief: Dr. Maode Ma
Abstracting/ Indexing: EI (INSPEC, IET), Google Scholar, Crossref, Engineering & Technology Digital Library, ProQuest, and Electronic Journals Library
E-mail: ijcce@iap.org
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