Analysis of Real Time ECG Signal using Principal Component Analysis

Principal Component Analysis (PCA) is one of the most valuable results oriented techniques of applied linear algebra. The minimum effort of PCA provides a roadmap for reducing a complex data set to a lower dimension to reveal the sometimes hidden, simplified structure that often underlie it. Bioelectrical signals express the electrical functionality of different organs in the human body. The Electrocardiogram, also called ECG signal, is one important signal among all bioelectrical signals. The ECG reflects the performance and the properties of the human heart and conveys very important hidden information in its structure. Principal component analysis (PCA) is a statistical technique whose purpose is to condense the information of a large set of correlated variables into a few variables (“principal components”), while not throwing overboard the variability present in the data set. The principal components are derived as a linear combination of the variables of the data set, with weights chosen so that the principal components become mutually uncorrelated. Each component contains new information about the data set, and is ordered so that the first few components account for most of the variability. Noise reduction is closely related to data compression as reconstruction of the original signal usually involves a set of eigenvectors whose noise level is low, and thus the reconstructed signal becomes low noise, such reduction is, however, mostly effective for noise with muscular origin. The purpose of the paper is to provide an overview of PCA in ECG signal compression. Keywords—ECG, Principal component analysis.