Abstract
This paper reviews the current status of principal component analysis in the area of ECG signal processing. The fundamentals of PCA are briefly described and the relationship between PCA and Karhunen-Loève transform is explained. Aspects on PCA related to data with temporal and spatial correlations are considered as adaptive estimation of principal components is. Several ECG applications are reviewed where PCA techniques have been successfully employed, including data compression, ST-T segment analysis for the detection of myocardial ischemia and abnormalities in ventricular repolarization, extraction of atrial fibrillatory waves for detailed characterization of atrial fibrillation, and analysis of body surface potential maps.
Highlights
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 [1]
Classification of waveform morphologies in arrhythmia monitoring is another early application of PCA, in which a subset of the principal components serves as features which are used to distinguish between normal sinus beats and abnormal waveforms such as premature ventricular beats
The purpose of the present paper is to provide an overview of PCA in ECG signal processing
Summary
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 [1]. 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. PCA is performed on a set of time samples rather than on a data set of variables. Signal processing is employed to deal with diverse issues in ECG analysis such as data compression, beat detection and classification, noise reduction, signal separation, and feature extraction. Classification of waveform morphologies in arrhythmia monitoring is another early application of PCA, in which a subset of the principal components serves as features which are used to distinguish between normal sinus beats and abnormal waveforms such as premature ventricular beats
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