title: Optimization of novel spectral estimator for fractionated electrogram analysis is helpful to discern atrial fibrillation type.
abstract: BACKGROUND
  Paroxysmal versus persistent atrial fibrillation (AF) can be distinguished based on differences in the spectral parameters of fractionated atrial electrograms. Maximization of these differences would improve characterization of the arrhythmogenic substrate. A novel spectral estimator (NSE) has been shown previously to provide greater distinction in AF spectral parameters as compared with the Fourier transform estimator. Herein, it is described how the differences in NSE spectral parameters can be further improved.
  
  
  METHODS
  In 10 persistent and 9 paroxysmal AF patients undergoing electrophysiologic study, fractionated electrograms were acquired from the distal bipolar ablation electrode. A total of 204 electrograms were recorded from the pulmonary vein (PV) antra and from the anterior and posterior left atrial free wall. The following spectral parameters were measured: the dominant frequency (DF), which reflects local activation rate, the DF amplitude (DA), and the mean spectral profile (MP), which represents background electrical activity. To optimize differences in parameters between paroxysmal versus persistent AF patients, the NSE was varied by selectively removing subharmonics, using a threshold. The threshold was altered in steps to determine the optimal subharmonics removal.
  
  
  RESULTS
  At the optimal threshold level, mean differences in persistent versus paroxysmal AF spectral parameters were: ΔDA=+0.371 mV, ΔDF=+0.737 Hz, and ΔMP=-0.096 mV. When subharmonics were not removed, the differences were substantially less: ΔDA=+0.301 mV, ΔDF=+0.699 Hz, and ΔMP=-0.063 mV.
  
  
  CONCLUSIONS
  NSE optimization produces greater spectral parameter difference between persistent versus paroxysmal AF data. Quantifying spectral parameter differences can be assistive in characterizing the arrhythmogenic substrate.
authors.affiliation: Department of Medicine, Division of Cardiology, Columbia University Medical Center, New York, USA. Electronic address: ciaccio@columbia.edu.
authors.forename: Edward J
authors.initials: EJ
authors.lastname: Ciaccio
authors.affiliation: Department of Medicine, Division of Cardiology, Columbia University Medical Center, New York, USA.
authors.forename: Angelo B
authors.initials: AB
authors.lastname: Biviano
authors.affiliation: Department of Medicine, Division of Cardiology, Columbia University Medical Center, New York, USA.
authors.forename: Hasan
authors.initials: H
authors.lastname: Garan
mesh_terms: D000465:Algorithms; D001281:Atrial Fibrillation / Q000145:classification* / Q000175:diagnosis*; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D003937:Diagnosis, Differential; D004562:Electrocardiography / Q000379:methods*; D006801:Humans; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted
keywords: Atrial fibrillation; Dominant frequency; Electrograms; Parameters; Spectral estimation