title: Automatic detection of atrial fibrillation using stationary wavelet transform and support vector machine.
abstract: BACKGROUND
  Atrial fibrillation (AF) is the most common cardiac arrhythmia, and a major public health burden associated with significant morbidity and mortality. Automatic detection of AF could substantially help in early diagnosis, management and consequently prevention of the complications associated with chronic AF. In this paper, we propose a novel method for automatic AF detection.
  
  
  METHODS
  Stationary wavelet transform and support vector machine have been employed to detect AF episodes. The proposed method eliminates the need for P-peak or R-Peak detection (a pre-processing step required by many existing algorithms), and hence its performance (sensitivity, specificity) does not depend on the performance of beat detection. The proposed method has been compared with those of the existing methods in terms of various measures including performance, transition time (detection delay associated with transitioning from a non-AF to AF episode), and computation time (using MIT-BIH Atrial Fibrillation database).
  
  
  RESULTS
  Results of a stratified 2-fold cross-validation reveals that the area under the Receiver Operative Characteristics (ROC) curve of the proposed method is 99.5%. Moreover, the method maintains its high accuracy regardless of the choice of the parameters' values and even for data segments as short as 10s. Using the optimal values of the parameters, the method achieves sensitivity and specificity of 97.0% and 97.1%, respectively.
  
  
  CONCLUSIONS
  The proposed AF detection method has high sensitivity and specificity, and holds several interesting properties which make it a suitable choice for practical applications.
authors: Department of Computer Engineering and Computer Science, California State University, Long Beach, 1250 Bellflower Boulevard-MS 8302, Long Beach, CA 90840, USA. Electronic address: Shadnaz.Asgari@csulb.edu.
authors: Shadnaz
authors: S
authors: Asgari
authors: Department of Electrical Engineering, University of California, Los Angeles, 56-125B Engineering IV Building, Box 951594, Los Angeles, CA 90095, CA.
authors: Alireza
authors: A
authors: Mehrnia
authors: Department of Electrical Engineering, California State University, Long Beach, 1250 Bellflower Boulevard, Long Beach, CA 90840, CA.
authors: Maryam
authors: M
authors: Moussavi
mesh_terms: D000465:Algorithms; D001145:Arrhythmias, Cardiac / Q000175:diagnosis; D001281:Atrial Fibrillation / Q000175:diagnosis*; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D004562:Electrocardiography; D005583:Fourier Analysis; D006801:Humans; D012372:ROC Curve; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted; D060388:Support Vector Machine; D058067:Wavelet Analysis
keywords: Atrial fibrillation; Cardiac arrhythmia; Log-energy entropy; ROC curve analysis; Support vector machine; Wavelet transform