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Principal Component Analysis in ECG Signal Processing

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.

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Correspondence to Francisco Castells.

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Castells, F., Laguna, P., Sörnmo, L. et al. Principal Component Analysis in ECG Signal Processing. EURASIP J. Adv. Signal Process. 2007, 074580 (2007). https://doi.org/10.1155/2007/74580

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Keywords

  • Ischemia
  • Principal Component Analysis
  • Atrial Fibrillation
  • Myocardial Ischemia
  • Quantum Information