title: Detection of Atrial fibrillation from non-episodic ECG data: a review of methods.
abstract: Atrial fibrillation (A-fib) is the most common cardiac arrhythmia. To effectively treat or prevent A-fib, automatic A-fib detection based on Electrocardiograph (ECG) monitoring is highly desirable. This paper reviews recently developed techniques for A-fib detection based on non-episodic surface ECG monitoring data. A-fib detection methods in the literature can be mainly classified into three categories: (1) time domain methods; (2) frequency domain methods; and (3) non-linear methods. In general the performances of these methods were evaluated in terms of sensitivity, specificity and overall detection accuracy on the datasets from the Physionet repository. Based on our survey, we conclude that no promising A-fib detection method that performs consistently well across various scenarios has been proposed yet.
authors.affiliation: School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore. E070067@ntu.edu.sg
authors.forename: Sujit Kumar
authors.initials: SK
authors.lastname: Sahoo
authors.forename: Wenmiao
authors.initials: W
authors.lastname: Lu
authors.forename: Sintiani Dewi
authors.initials: SD
authors.lastname: Teddy
authors.forename: Desok
authors.initials: D
authors.lastname: Kim
authors.forename: Mengling
authors.initials: M
authors.lastname: Feng
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D001281:Atrial Fibrillation / Q000175:diagnosis*; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D004562:Electrocardiography / Q000379:methods*; D006801:Humans; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity