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.

Detection of Atrial Fibrillation in Compressively Sensed Electrocardiogram for Remote Monitoring

“…These techniques require the participant to be in the camera's field of view limiting their application to stationary patients. ECG recordings are typically used to detect and diagnose AF and are considered the clinical gold standard (Kumar Sahoo et al, 2011). ECG is typically recoded using electrodes placed on the patient's torso, however armband electrodes have been recently suggested (Lazaro et al, 2020).…”

Monitoring of heart rate and inter-beat intervals with wrist plethysmography in patients with atrial fibrillation

“…The recognition of AF is based on two characteristic phenomena in the ECG: the absence of p-wave and irregularity of the heartbeat intervals. The analysis is typically classified into three categories: first time domain methods, second frequency domain methods and third non-linear methods (Sahoo et al 2011). In contrast, in the analysis of PPG, the p-wave cannot be seen or analyzed.…”