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

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.