title: Classification of atrial fibrillation episodes from sparse electrocardiogram data.
abstract: BACKGROUND
  Atrial fibrillation (AF) is the most common form of cardiac arrhythmia. This paper presents the application of the Classification and Regression Tree (CART) technique for detecting spontaneous termination or sustenance of AF with sparse data.
  
  
  METHODS
  Electrocardiogram (ECG) recordings were obtained from the PhysioNet (AF Termination Challenge Database 2004) Web site. Signal analysis, feature extraction, and classification were made to distinguish among 3 AF episodes, namely, Nonterminating (N), Soon (<1 minute) to be terminating (S), and Terminating immediately (<1 second) (T).
  
  
  RESULTS
  A continuous wavelet transform whose basis functions match the EKG patterns was found to yield compact representation (approximately 2 orders of magnitude). This facilitates the development of efficient algorithms for beat detection, QRST subtraction, and multiple ECG quantifier extraction (eg, QRS width, QT interval). A compact feature set was extracted through principal component analysis of these quantifiers. Accuracies exceeding 90% for AF episode classification were achieved.
  
  
  CONCLUSIONS
  A wavelet representation customized to the ECG signal pattern was found to yield 98% lower entropies compared with other representations that use standard library wavelets. The Classification and Regression Tree (CART) technique seems to distinguish the N vs T, and the S vs T classifications very accurately.
authors: Industrial Engineering and Management, Oklahoma State University, Stillwater, OK, USA.
authors: Satish
authors: S
authors: Bukkapatnam
authors: Ranga
authors: R
authors: Komanduri
authors: Hui
authors: H
authors: Yang
authors: Prahalad
authors: P
authors: Rao
authors: Wen-Chen
authors: WC
authors: Lih
authors: Milind
authors: M
authors: Malshe
authors: Lionel M
authors: LM
authors: Raff
authors: Bruce
authors: B
authors: Benjamin
authors: Mark
authors: M
authors: Rockley
mesh_terms: D000465:Algorithms; D001281:Atrial Fibrillation / Q000175:diagnosis*; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D004562:Electrocardiography / Q000379:methods*; D006801:Humans; D008955:Models, Cardiovascular; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity