A comparative analysis of principal component and independent component techniques for electrocardiograms

Principal component analysis (PCA) is used for ECG data compression, denoising and decorrelation of noisy and useful ECG components or signals. In this study, a comparative analysis of independent component analysis (ICA) and PCA for correction of ECG signals is carried out by removing noise and artifacts from various raw ECG data sets. PCA and ICA scatter plots of various chest and augmented ECG leads and their combinations are plotted to examine the varying orientations of the heart signal. In order to qualitatively illustrate the recovery of the shape of the ECG signals with high fidelity using ICA, corrected source signals and extracted independent components are plotted. In this analysis, it is also investigated if difference between the two kurtosis coefficients is positive than on each of the respective channels and if we get a super-Gaussian signal, or a sub-Gaussian signal. The efficacy of the combined PCA–ICA algorithm is verified on six channels V1, V3, V6, AF, AR and AL of 12-channel ECG data. ICA has been utilized for identifying and for removing noise and artifacts from the ECG signals. ECG signals are further corrected by using statistical measures after ICA processing. PCA scatter plots of various ECG leads give different orientations of the same heart information when considered for different combinations of leads by quadrant analysis. The PCA results have been also obtained for different combinations of ECG leads to find correlations between them and demonstrate that there is significant improvement in signal quality, i.e., signal-to-noise ratio is improved. In this paper, the noise sensitivity, specificity and accuracy of the PCA method is evaluated by examining the effect of noise, base-line wander and their combinations on the characteristics of ECG for classification of true and false peaks.