Principal Component Analysis in ECG Signal Processing
- PDF / 4,423,742 Bytes
- 21 Pages / 600.03 x 792 pts Page_size
- 28 Downloads / 265 Views
Research Article Principal Component Analysis in ECG Signal Processing 3 Andreas Bollmann,4 and Jose ´ Millet Roig5 ¨ Francisco Castells,1 Pablo Laguna,2 Leif Sornmo, 1 Grupo
de Investigaci´on en Bioingener´ıa, Electr´onica y Telemedicina, Departamento de Ingener´ıa Electr´onica, Escuela Polit´ecnica Superior de Gand´ıa, Universidad Polit´ecnica de Valencia (UPV), Ctra. Nazaret-Oliva, 46730 Gand´ıa, Spain 2 Communications Technology Group, Arag´ on Institute of Engineering Research, University of Zaragoza, 50018 Zaragoza, Spain 3 Signal Processing Group, Department of Electrical Engineering, Lund University, 22100 Lund, Sweden 4 Department of Cardiology, Otto-von-Guericke-University Magdeburg, 39120 Magdeburg, Germany 5 Grupo de Investigaci´ on en Bioingener´ıa, Electr´onica y Telemedicina, Departamento de Ingener´ıa Electr´onica, Universidad Polit´ecnica de Valencia, Cami de Vera, 46022 Valencia, Spain Received 11 May 2006; Revised 20 November 2006; Accepted 20 November 2006 Recommended by William Allan Sandham 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-Lo`eve 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. Copyright © 2007 Francisco Castells et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
1.
INTRODUCTION
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. In signal processing applications, PCA is performed on a set of time samples rather than on a data set of variables. When the signal is recurrent in nature, like the ECG signal, the analysis is often based on samples extracted from the same segment location of different periods of the signal. Signal processing is today found in virtually any system for ECG analysis, and has clearly demonstrated its importance for achieving improved d
Data Loading...
