PCA and ICA processing methods for removal of artifacts and noise in electrocardiograms: A survey and comparison

• Authors: M. P. S. Chawla
• Venue: Applied Soft Computing
• Year: 2011

Electrocardiogram (ECG) signals are affected by various kinds of noise and artifacts that may hide important information of interest. Wavelet transform (WT) technique is used to identify the characteristic points of the electrocardiogram (ECG) signal with fairly good accuracy, even in the presence of severe high frequency and low frequency noise. Independent component analysis (ICA) is a new technique suitable for separating independent components from ECG complex signals, whereas principal component analysis (PCA) is used to reduce dimensionality and for feature extraction of the ECG data prior to or at times after performing ICA in special circumstances. In this analysis, PCA is analyzed from three points of view, variance maximization, the singular value decomposition and ECG data compression. The sensitivity of the different ECG components with respect to the ECG data dimensions has been studied using PCA screen plots. The validity and performance of the approaches used are confirmed through computer simulations on common standards for electrocardiography (CSE) base ECG data. Standard or instantaneous ICA, which is the most commonly, accepted ICA technique is first compared with PCA technique and then with constrained ICA, which enables the estimation of only one component close to a particular reference ECG signal. The ICA method can also be extended for QRS detection and reference signal generation, using constrained ICA and as well for multichannel ECG separation after removing noise and artifacts, further favoring segment classification. The results were obtained using Matlab environment. Using composite WT based PCA-ICA methods helps for redundant data reduction as well for better feature extraction. The efficacy of the combined PCA-ICA algorithm lies on the fact that the location of the R-peaks is accurately determined, and none of the peaks are ignored or missed, as Quadratic Spline wavelet is also used.