Applications of complexity analysis in clinical heart failure

  title={Applications of complexity analysis in clinical heart failure},
  author={C. Liu and A. Murray},
Heart failure is known to influence heart rhythm in patients. Complexity analysis techniques, including techniques associated with entropy, have great potential for providing a better understanding of cardiac rhythms, and for helping research in this area. We review the analysis principles of conventional time-domain analysis, frequency-domain analysis and of newer complexity analysis. We then illustrate the techniques using real clinical data, allowing a comparison of the techniques, and also… Expand

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  • V. Tuzcu, Selman Nas, Tülay Börklü, A. Ugur
  • Medicine
  • Europace : European pacing, arrhythmias, and cardiac electrophysiology : journal of the working groups on cardiac pacing, arrhythmias, and cardiac cellular electrophysiology of the European Society of Cardiology
  • 2006
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