Identification of ECG Arrhythmias Using Phase Space Reconstruction

  • Felice M. Roberts
  • Richard J. Povinelli
  • Kristina M. Ropella
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2168)


Changes in the normal rhythmicity of a human heart may result in different cardiac arrhythmias, which may be immediately fatal or cause irreparable damage to the heart when sustained over long periods of time. The ability to automatically identify arrhythmias from ECG recordings is important for clinical diagnosis and treatment, as well as, for understanding the electrophysiological mechanisms of the arrhythmias. This paper proposes a novel approach to efficiently and accurately identify normal sinus rhythm and various ventricular arrhythmias through a combination of phase space reconstruction and machine learning techniques. Data was recorded from patients experiencing spontaneous arrhythmia, as well as, induced arrhythmia. The phase space attractors of the different rhythms were learned from both inter- and intra-patient arrhythmic episodes. Out-of-sample ECG rhythm recordings were classified using the learned attractor probability distributions with an overall accuracy of 83.0%.


Phase Space Sinus Rhythm Ventricular Fibrillation Complexity Measure Ventricular Tachyarrhythmia 
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.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2001

Authors and Affiliations

  • Felice M. Roberts
    • 1
  • Richard J. Povinelli
    • 1
  • Kristina M. Ropella
    • 2
  1. 1.Department of Electrical and Computer EngineeringMarquette UniversityMilwaukee
  2. 2.Department of Biomedical EngineeringMarquette UniversityMilwaukee

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