Background: Atrial fibrillation (AF) is the world's most common heart rhythm disorder and even several minutes of AF episodes can contribute to risk for complications, including stroke. However, AF often goes undiagnosed owing to the fact that it can be paroxysmal, brief, and asymptomatic.
Objective: To facilitate better AF monitoring, we studied the feasibility of AF detection using a continuous electrocardiogram (ECG) signal recorded from a novel wearable armband device.
Methods: In our 2-step algorithm, we first calculate the R-R interval variability-based features to capture randomness that can indicate a segment of data possibly containing AF, and subsequently discriminate normal sinus rhythm from the possible AF episodes. Next, we use density Poincaré plot-derived image domain features along with a support vector machine to separate premature atrial/ventricular contraction episodes from any AF episodes. We trained and validated our model using the ECG data obtained from a subset of the MIMIC-III (Medical Information Mart for Intensive Care III) database containing 30 subjects.
Results: When we tested our model using the novel wearable armband ECG dataset containing 12 subjects, the proposed method achieved sensitivity, specificity, accuracy, and F1 score of 99.89%, 99.99%, 99.98%, and 0.9989, respectively. Moreover, when compared with several existing methods with the armband data, our proposed method outperformed the others, which shows its efficacy.
Conclusion: Our study suggests that the novel wearable armband device and our algorithm can be used as a potential tool for continuous AF monitoring with high accuracy.
Keywords: Accuracy; Armband; Atrial fibrillation; Classification; Entropy; Feature selection; Premature atrial contraction; Template; Wavelet.
© 2021 Heart Rhythm Society.