title: Developing an atrial activity-based algorithm for detection of atrial fibrillation.
abstract: In this study we propose a novel atrial activity-based method for atrial fibrillation (AF) identification that detects the absence of normal sinus rhythm (SR) P-waves from the surface ECG. The proposed algorithm extracts nine features from P-waves during SR and develops a statistical model to describe the distribution of the features. The Expectation-Maximization algorithm is applied to a training set to create a multivariate Gaussian Mixture Model (GMM) of the feature space. This model is used to identify P-wave absence (PWA) and, in turn, AF. An optional post-processing stage, which takes a majority vote of successive outputs, is applied to improve classier performance. The algorithm was tested on 20 records in the MIT-BIH Atrial Fibrillation Database. Classification combining seven beats showed a sensitivity of 99.28%, a specificity of 90.21%. The presented algorithm has a classification performance comparable to current Heartrate-based algorithms yet is rate-independent and capable of making an AF determination in a few beats.
authors.forename: Steven
authors.initials: S
authors.lastname: Ladavich
authors.forename: Behnaz
authors.initials: B
authors.lastname: Ghoraani
mesh_terms: D000465:Algorithms; D001281:Atrial Fibrillation / Q000503:physiopathology*; D016208:Databases, Factual; D006325:Heart Atria / Q000503:physiopathology*; D006339:Heart Rate; D006801:Humans; D012372:ROC Curve