Finding new EASI ECG coefficients: Improving EASI ECG model using various regression techniques

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 Machine Regression (with Sequential Minimal Optimization algorithm), Linear Regression and Pace Regression methods were used to improve the quality of the 12-lead electrocardiogram derived from four (EASI) electrodes. Hundreds of ANNs with different learning rates and number of hidden layers were built and tested using data from PhysioNet and also data that were artificially generated. Next SMO Regression method with few different kernels (polynomial, normalized polynomial and RBF), Linear Regression and Pace Regression method were tested on the same dataset. All computed results were compared with those obtained using classic EASI ECG method described by Dover. Computation of Root Mean Squared Error and Correlation Coefficient was performed to measure the overall result of a given method. Obtained results show that various regression methods could be used to increase the performance of EASI ECG method and thus make it more popular.