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Principal Component Analysis in ECG Signal Processing

  • Francisco Castells1Email author,
  • Pablo Laguna2,
  • Leif Sörnmo3,
  • Andreas Bollmann4 and
  • José Millet Roig5
EURASIP Journal on Advances in Signal Processing20072007:074580

https://doi.org/10.1155/2007/74580

Received: 11 May 2006

Accepted: 20 November 2006

Published: 8 February 2007

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.

Keywords

IschemiaPrincipal Component AnalysisAtrial FibrillationMyocardial IschemiaQuantum Information

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Authors’ Affiliations

(1)
Grupo de Investigación en Bioingenería, Electrónica y Telemedicina, Departamento de Ingenería Electrónica, Escuela Politécnica Superior de Gandía, Universidad Politécnica de Valencia (UPV), Ctra. Nazaret-Oliva, Gandía, Spain
(2)
Communications Technology Group, Aragón Institute of Engineering Research, University of Zaragoza, Zaragoza, Spain
(3)
Signal Processing Group, Department of Electrical Engineering, Lund University, Lund, Sweden
(4)
Department of Cardiology, Otto-von-Guericke-University Magdeburg, Magdeburg, Germany
(5)
Grupo de Investigación en Bioingenería, Electrónica y Telemedicina, Departamento de Ingenería Electrónica, Universidad Politécnica de Valencia, Valencia, Spain

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Copyright

© Francisco Castells et al. 2007

This article is published under license to BioMed Central Ltd. 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.

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