title: Detecting instabilities of cardiac rhythm.
abstract: Diminished beat-to-beat variations in cardiac cycle lengths (CLs) are associated with poor prognosis after acute myocardial infarction and in patients with heart failure. Short-long-short sequences of cardiac cycles, or ultra-short rhythm instabilities, precede initiation of ventricular tachyarrhythmias in some patients. However, little is known about clinical or prognostic significance of abrupt short-term instabilities in CL (AICL) that occur minutes to hours before the event, in part because appropriate analytical methods are lacking. Although various techniques have been used to analyze CL changes, methods for analysis of AICL are limited. We compared performance of time domain, spectral, nonlinear, and pattern recognition techniques with respect to the detection and quantification of AICL. Because of high intra- and inter-subject variability of CL, pattern recognition techniques compared favorably to other studied methods. In continuous ambulatory ECG recordings, AICL occurred hours before spontaneous initiation of sustained atrial and ventricular arrhythmias in different patient populations. AICL were also found prior to the onset of spontaneous ventricular arrhythmias in a mouse model of congestive heart failure. To quantify AICL, we used the number of unstable orthogonal projection coefficients; this number gradually increased hours before the event. Removal of ectopic beats reduced but did not eliminate AICL. To illustrate potential physiological effects and temporal evolution of AICL, we used a simple, continuous, two-dimensional model of cardiac tissue governed by the Morris-Lecar equations. Computer simulations in this model showed that AICL may lead to gradual accumulation of spatial irregularities of the propagation wavefront giving rise to the initiation of reentry. Time-frequency analysis of the most significant eigenvectors of cardiac rhythm in subjects undergoing head-up tilt showed that AICL could indicate instabilities and unsuccessful adaptation of autonomic nervous system activity to physiological stimuli.
authors.affiliation: University of Pittsburgh, PA 15213, USA. shustermanv@msx.upmc.edu
authors.forename: Vladimir
authors.initials: V
authors.lastname: Shusterman
authors.forename: Benhur
authors.initials: B
authors.lastname: Aysin
authors.forename: G Bard
authors.initials: GB
authors.lastname: Ermentrout
authors.forename: Barry
authors.initials: B
authors.lastname: London
authors.forename: David
authors.initials: D
authors.lastname: Schwartzman
mesh_terms: D000818:Animals; D001145:Arrhythmias, Cardiac / Q000503:physiopathology*; D001281:Atrial Fibrillation / Q000503:physiopathology; D005117:Cardiac Complexes, Premature / Q000503:physiopathology; D003198:Computer Simulation; D004562:Electrocardiography; D015716:Electrocardiography, Ambulatory; D006333:Heart Failure / Q000503:physiopathology; D006339:Heart Rate / Q000502:physiology*; D006801:Humans; D008433:Mathematics; D051379:Mice; D008962:Models, Theoretical; D010363:Pattern Recognition, Automated; D017180:Tachycardia, Ventricular / Q000503:physiopathology