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Noninvasive Cardiac Signal Analysis Using Data Decomposition Techniques

  • Vicente Zarzoso
  • Olivier Meste
  • Pierre Comon
  • Decebal Gabriel Latcu
  • Nadir Saoudi
Chapter

Abstract

Analyzing electrocardiogram (ECG) recordings is crucial to gathering useful information about a patient’s heart condition in a noninvasive manner, with the consequent benefits in procedural time, cost and risk of complications relative to invasive diagnostic modalities. Signal processing can aid cardiologists to make informed decisions by revealing and quantifying underlying structures that may not be apparent in the observed data, especially when the redundancy inherent to the ECG leads hinders the expert’s analysis. This chapter considers two such ECG signal processing problems, namely, T-wave alternans detection and atrial activity estimation during atrial fibrillation. The redundancy present in the ECG can effectively be exploited by decomposing the observed data into interesting signals or components that are often easier to analyze than the original recording. These components can be determined as appropriate linear combinations of the observed data according to different criteria such as principal component analysis (PCA) and independent component analysis (ICA). While PCA relies on second-order statistics and yields uncorrelated components, ICA achieves independence through the use of higher-order statistics. The main concepts as well as some advantages, limitations and success stories of these popular decomposition techniques are illustrated on real ECG data from the above cardiac signal processing problems.

Keywords

Principal Component Analysis Atrial Fibrillation Mean Square Error Independent Component Analysis Principal Direction 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgements

Part of the work summarized in this chapter is supported by the French National Research Agency under contract ANR 2010 JCJC 0303 01 “PERSIST”.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Vicente Zarzoso
    • 1
  • Olivier Meste
    • 1
  • Pierre Comon
    • 2
  • Decebal Gabriel Latcu
    • 3
  • Nadir Saoudi
    • 3
  1. 1.I3S – UMR 7271 CNRS/UNS, Algorithmes-Euclide-BSophia AntipolisFrance
  2. 2.GIPSA-Lab - UMR 5216St Martin d’HèresFrance
  3. 3.Cardiology DepartmentPrincess Grace HospitalMonaco CedexFrance

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