A deep learning algorithm using RR interval signals accurately discriminated atrial fibrillation, atrial flutter, and normal sinus rhythm with 99.98% accuracy, 100% sensitivity, and 99.94% specificity.
Does a deep learning algorithm accurately discriminate AFIB, AFL, and NSR using RR interval signals in 4051 subjects?
A deep learning algorithm can highly accurately detect and discriminate atrial fibrillation, atrial flutter, and normal sinus rhythm using cost-effective RR interval signals.
Effect estimate: ACC = 99.98%, SEN = 100.00%, SPE = 99.94%
Abnormal heart rhythms, also known as arrhythmias, can be life-threatening. AFIB and AFL are examples of arrhythmia that affect a growing number of patients. This paper describes a method that can support clinicians during arrhythmia diagnosis. We propose a deep learning algorithm to discriminate AFIB, AFL, and NSR RR interval signals. The algorithm was designed with data from 4051 subjects. With 10-fold cross-validation, the algorithm achieved the following results: ACC = 99.98%, SEN = 100.00%, and SPE = 99.94%. These results are significant because they show that it is possible to automate arrhythmia detection in RR interval signals. Such a detection method makes economic sense because RR interval signals are cost-effective to measure, communicate, and process. Having such a cost-effective solution might lead to widespread long-term monitoring, which can help detecting arrhythmia earlier. Detection can lead to treatment, which improves outcomes for patients.
Faust et al. (Tue,) conducted a other in Arrhythmia (Atrial Fibrillation, Atrial Flutter, Normal Sinus Rhythm) (n=4,051). Deep learning algorithm for RR interval signals was evaluated on Discrimination of AFIB, AFL, and NSR RR interval signals (ACC = 99.98%, SEN = 100.00%, SPE = 99.94%). A deep learning algorithm using RR interval signals accurately discriminated atrial fibrillation, atrial flutter, and normal sinus rhythm with 99.98% accuracy, 100% sensitivity, and 99.94% specificity.