title: Atrial near-field and ventricular far-field analysis by automated signal processing at rest and during exercise.
abstract: BACKGROUND
  Sophisticated monitoring of atrial activity is a prerequisite for modern pacemaker therapy. Ideally, near-fields and ventricular far-fields ought to be distinguished by beat-to-beat template analysis of the atrial signal. A prerequisite is that atrial signals are stable under different conditions.
  
  
  RESULTS
  A Matlab routine was developed to analyze atrial electrograms of 23 patients at least 3 months after implantation of a dual chamber pacemaker under several conditions including at rest, bipolar at rest, in an upright position, during treadmill exercise, and postexercise. A near-field and far-field template was created and amplitudes, widths, and slew rates were measured. In bipolar configuration, near-field amplitude at rest was 3.04 +/- 0.94 mV (unipolar)/3.36 +/- 1.0 mV (bipolar) versus 3.18 +/- 1.0 mV (bipolar) at peak exercise. Far-field amplitude at rest was 1.66 +/- 1.18 (unipolar)/0.47 +/- 0.27 mV (bipolar) and 0.41 +/- 0.21 mV (bipolar) at peak exercise (n.s. for bipolar measurements). No overall significant changes were observed for near- and far-field widths and slew rates during exercise. Shorter tip-ring distances of the atrial bipole, lead position, and the presence of sinus node disease did not have any impact on overall near- and far-field signal characteristics. Intraindividual differences between rest and peak exercise were moderate (range: near-field +0.15 to -0.54 mV; range: far-field +0.05 to -0.18 mV).
  
  
  CONCLUSIONS
  Atrial near and far fields can be automatically classified and quantified by automated signal processing. Signals did not change during exercise or change of posture. This is a prerequisite for the implementation of beat-to-beat template analysis into pacemakers.
authors.affiliation: University Hospital Schleswig Holstein, Campus Luebeck, Medicine II, Luebeck, Germany. eberhard@uni-luebeck.de
authors.forename: Frank
authors.initials: F
authors.lastname: Eberhardt
authors.forename: Hendrik
authors.initials: H
authors.lastname: Bonnemeier
authors.forename: Martin
authors.initials: M
authors.lastname: Lipphardt
authors.forename: Ulrich G
authors.initials: UG
authors.lastname: Hofmann
authors.forename: Heribert
authors.initials: H
authors.lastname: Schunkert
authors.forename: Uwe K H
authors.initials: UK
authors.lastname: Wiegand
mesh_terms: D000368:Aged; D000465:Algorithms; D001281:Atrial Fibrillation / Q000175:diagnosis* / Q000503:physiopathology; D004562:Electrocardiography; D005080:Exercise Test / Q000379:methods*; D005240:Feasibility Studies; D005260:Female; D006801:Humans; D008297:Male; D010138:Pacemaker, Artificial; D011187:Posture; D012815:Signal Processing, Computer-Assisted; D018709:Statistics, Nonparametric; D014693:Ventricular Fibrillation / Q000175:diagnosis* / Q000503:physiopathology