Nonlinear dynamics analysis of electrocardiograms for detection of coronary artery disease

  • Authors:

  • Karolis Antanavičius;Algirdas Bastys;Juozas Bluas;Liudas Gargasas;Svetlana Kaminskien;Graina Urbonavičien;Alfonsas Vainoras

  • Affiliations:

  • Vilnius University, Naugarduko str. 24, LT-03225 Vilnius, Lithuania;Vilnius University, Naugarduko str. 24, LT-03225 Vilnius, Lithuania;Institute of Cardiology, Kaunas University of Medicine, Sukilli av. 17, LT-50161 Kaunas, Lithuania;Institute of Cardiology, Kaunas University of Medicine, Sukilli av. 17, LT-50161 Kaunas, Lithuania;Institute of Cardiology, Kaunas University of Medicine, Sukilli av. 17, LT-50161 Kaunas, Lithuania;Institute of Cardiology, Kaunas University of Medicine, Sukilli av. 17, LT-50161 Kaunas, Lithuania;Institute of Cardiology, Kaunas University of Medicine, Sukilli av. 17, LT-50161 Kaunas, Lithuania

  • Venue:
  • Computer Methods and Programs in Biomedicine
  • Year:
  • 2008

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Abstract

A computerized approach of nonlinear dynamics analysis of electrocardiogram (ECG) signals was applied for the detection of coronary artery disease (CAD). The proposed nonlinear dynamics descriptors were derived from 12-lead rest ECG data, and evaluated by originally developed computer software. Fluctuations of potentials of ECG leads that occur during the period of 20ms with a magnitude of 5-20@mV were significantly less beat-to-beat predictable in ischemic versus non-ischemic patients. The well-known nonlinear dynamics descriptors, recurrences percentage, mutual information, fractal dimension, and a new descriptor, next embedding dimension error, were good quantitative descriptors of fluctuations. They were significantly different (