title: Classification of cardiac rhythm using heart rate dynamical measures: validation in MIT-BIH databases.
abstract: BACKGROUND
  Identification of atrial fibrillation (AF) is a clinical imperative. Heartbeat interval time series are increasingly available from personal monitors, allowing new opportunity for AF diagnosis.
  
  
  OBJECTIVE
  Previously, we devised numerical algorithms for identification of normal sinus rhythm (NSR), AF, and SR with frequent ectopy using dynamical measures of heart rate. Here, we wished to validate them in the canonical MIT-BIH ECG databases.
  
  
  METHODS
  We tested algorithms on the NSR, AF and arrhythmia databases.
  
  
  RESULTS
  When the databases were combined, the positive predictive value of the new algorithms exceeded 95% for NSR and AF, and was 40% for SR with ectopy. Further, dynamical measures did not distinguish atrial from ventricular ectopy. Inspection of individual 24hour records showed good correlation of observed and predicted rhythms.
  
  
  CONCLUSIONS
  Heart rate dynamical measures are effective ingredients in numerical algorithms to classify cardiac rhythm from the heartbeat intervals time series alone.
authors: Department of Electronics, Information and Bioengineering, Politecnico di Milano, P.zza Leonardo da Vinci 32, Milan, Italy.
authors: Marta
authors: M
authors: Carrara
authors: Department of Electronics, Information and Bioengineering, Politecnico di Milano, P.zza Leonardo da Vinci 32, Milan, Italy.
authors: Luca
authors: L
authors: Carozzi
authors: Department of Medicine, University of Virginia, Box 800158, Charlottesville, VA, USA.
authors: Travis J
authors: TJ
authors: Moss
authors: Department of Electronics, Information and Bioengineering, Politecnico di Milano, P.zza Leonardo da Vinci 32, Milan, Italy.
authors: Marco
authors: M
authors: de Pasquale
authors: Department of Electronics, Information and Bioengineering, Politecnico di Milano, P.zza Leonardo da Vinci 32, Milan, Italy.
authors: Sergio
authors: S
authors: Cerutti
authors: Department of Medicine, University of Virginia, Box 800158, Charlottesville, VA, USA.
authors: Douglas E
authors: DE
authors: Lake
authors: Department of Medicine, University of Virginia, Box 800158, Charlottesville, VA, USA.
authors: J Randall
authors: JR
authors: Moorman
authors: Department of Electronics, Information and Bioengineering, Politecnico di Milano, P.zza Leonardo da Vinci 32, Milan, Italy. Electronic address: manuela.ferrario@polimi.it.
authors: Manuela
authors: M
authors: Ferrario
mesh_terms: D000465:Algorithms; D001281:Atrial Fibrillation / Q000175:diagnosis* / Q000503:physiopathology*; D016208:Databases, Factual; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D004562:Electrocardiography / Q000379:methods*; D006339:Heart Rate; D006801:Humans; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity
keywords: Atrial ectopy; Atrial fibrillation; Heart rate variability; Sample entropy; Ventricular ectopy