title: Automatic detection of conduction block based on time-frequency analysis of unipolar electrograms.
abstract: It is commonly thought that lethal tachyarrhythmias, such as ventricular fibrillation (VF), are perpetuated by functional reentry, which occurs when an activation wave blocks and rotates around tissue that is excitable (i.e., functional block). Electrograms recorded near these regions typically contain two sequential deflections representing activation on either side of the block. By detecting these "double potentials," we hypothesize that functional block can be detected by a single electrode.
  
  
  METHODS
  Unipolar electrograms were recorded from a 24 x 21 mapping array on the intact ventricular epicardium of five pigs during electrically-induced VF. The short time Fourier Transform (STFT) of each electrogram was analyzed to identify double potentials. To evaluate the performance of the STFT algorithm, conduction block was located in activation maps using a minimum conduction velocity criterion (10 cm/s) and then compared to the results of the STFT algorithm.
  
  
  RESULTS
  The STFT algorithm detected conduction block with a sensitivity of 0.74 +/- 0.12 and a specificity of 0.99 +/- 0.00.
  
  
  CONCLUSIONS
  We have developed an automated algorithm that can detect functional block during VF from a single electrode recording. Possible applications include fast, objective identification of block in mapping data and realtime localization of reentrant substrates using mapping catheters.
authors.affiliation: Department of Medicine, University of Alabama at Birmingham 35294, USA.
authors.forename: F G
authors.initials: FG
authors.lastname: Evans
authors.forename: J M
authors.initials: JM
authors.lastname: Rogers
authors.forename: W M
authors.initials: WM
authors.lastname: Smith
authors.forename: R E
authors.initials: RE
authors.lastname: Ideker
mesh_terms: D000465:Algorithms; D000818:Animals; D004562:Electrocardiography; D004566:Electrodes; D005583:Fourier Analysis; D006327:Heart Block / Q000175:diagnosis; D009431:Neural Conduction; D012372:ROC Curve; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted; D013552:Swine; D014693:Ventricular Fibrillation / Q000175:diagnosis*