Improved EASI ECG model obtained using various machine learning and regression techniques

Author

Oleksy, Wojciech ; Tkacz, Ewaryst ; Budzianowski, Zbigniew

Author_Institution

Inst. of Inf., Silesian Univ. of Technol. in Gliwice, Gliwice, Poland

fYear

2012

fDate

3-4 July 2012

Firstpage

534

Lastpage

538

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), Support Vector Machines, Linear Regression, Pace Regression and Least Median of Squares Regression methods were used to improve the quality of the 12-lead electrocardiogram derived from four (EASI) electrodes.

Keywords

biomedical electrodes; electrocardiography; medical signal processing; multilayer perceptrons; regression analysis; support vector machines; EASI ECG method; EASI ECG model; EASI electrodes; artificial neural network; electrocardiogram; least median of squares regression; linear regression; machine learning techniques; multilayer perceptron; pace regression; regression techniques; standard 12 lead ECG; support vector machines; Electrocardiography; Electrodes; Kernel; Machine learning; Multilayer perceptrons; Standards; Support vector machines; Artificial Neural Network; EASI; ECG; Least Median of Squares Regression; Linear Regression; PACE regression; SVM;

fLanguage

English

Publisher

ieee

Conference_Titel

Telecommunications and Signal Processing (TSP), 2012 35th International Conference on

Conference_Location

Prague

Print_ISBN

978-1-4673-1117-5

Type

conf

DOI

10.1109/TSP.2012.6256352

Filename

6256352