Improving EASI ECG method using various regression techniquesa

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. Multilayer Perceptron (Artificial Neural Network), Sequential Minimal Optimization Regression and Linear Regression methods were used to improve the quality of the 12-lead electrocardiogram derived from four (EASI) electrodes. Computation of Root Mean Squared Error and Correlation Coefficient was performed to measure the overall result of a given method. The lowest RMSE of 20,90 and the highest correlation coefficient of 0,9858 were obtained using ANN method. Second best result was obtained for SMO Regression Method with Radial Basis Function kernel (RMSE equal to 25,97 and correlation coefficient of 0,9813). The least complex regression method of Linear Regression produced results on a level of 28,35 for RMSE and 0,9741 for correlation coefficient. Results obtained for classic Dover algorithm of deriving 12-lead ECG from EASI electrodes were much worse than those obtained for all regression methods. RMSE of 80,10, which is by around 59,19 higher than RMSE of ANN and correlation coefficient of 0,96, which is by 0,03 lower then correlation coefficient of ANN.