title: Single-beat analysis of ventricular late potentials in the surface electrocardiogram using the spectrotemporal pattern recognition algorithm in patients with coronary artery disease.
abstract: OBJECTIVE
  Post-infarction risk stratification can be ascertained from beat-to-beat variations in ventricular late potentials. However, gaining such information by conventional late potential analysis using signal averaging is still not possible.
  
  
  METHODS
  We therefore developed the spectrotemporal pattern recognition algorithm in order to detect beat-to-beat variations in late potentials. Based on the spectrotemporal pattern recognition algorithm two-dimensional correlation function, the typical spectral pattern of late potentials can be identified in spectrotemporal maps of single beats, even in the presence of noise.
  
  
  RESULTS
  Surface electrocardiograms of 385 patients after myocardial infarction (85 with documented sustained ventricular tachycardia (group 1), 100 with fast, polymorphic ventricular tachycardia (> 270 cycles.min-1) or primary ventricular fibrillation (group 2), 200 without ventricular arrhythmias (group 3) and 45 healthy volunteers (group 4), were analysed. The spectrotemporal pattern recognition algorithm detected late potentials in single beats in 89% of group 1 patients, in 79% of group 2, in 22% of group 3 and in 4% of normals. The spectrotemporal pattern recognition algorithm measured late potential frequency and extension of late potentials into the ST segment, which was significantly different between groups 1 and 2. Beat-to-beat variations in late potentials, with respect to frequency and extension into the ST segment, were markedly higher in patients with a history of primary ventricular fibrillation.
  
  
  CONCLUSIONS
  Single-beat analysis using the spectrotemporal pattern recognition algorithm may improve risk stratification of patients after myocardial infarction, and provides information on patients prone to ventricular fibrillation.
authors.affiliation: Medical Hospital I, University of Munich, Germany.
authors.forename: P
authors.initials: P
authors.lastname: Steinbigler
authors.forename: R
authors.initials: R
authors.lastname: Haberl
authors.forename: G
authors.initials: G
authors.lastname: Jilge
authors.forename: G
authors.initials: G
authors.lastname: Steinbeck
mesh_terms: D000200:Action Potentials; D000465:Algorithms; D003327:Coronary Disease / Q000503:physiopathology*; D004562:Electrocardiography; D006352:Heart Ventricles / Q000503:physiopathology*; D006801:Humans; D010363:Pattern Recognition, Automated; D018570:Risk Assessment; D012815:Signal Processing, Computer-Assisted