Key result
ResNet algorithm using RR intervals detects AF and atrial flutter with ~100% accuracy.
Why the study?
Arrhythmias such as AFIB and AFL affect a growing number of patients and can be life-threatening, requiring methods to support clinicians during diagnosis.
Effect estimate: Accuracy 99.98%, Sensitivity 100.00%, Specificity 99.94%
Automated deep learning analysis of simple RR intervals provides a highly accurate method for distinguishing atrial fibrillation and flutter, potentially enabling cost-effective long-term wearable monitoring.
May support wearable AF/AFL screening; hypothesis-generating pending external and prospective validation.
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.
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Faust et al. (2021) studied Atrial Fibrillation and Atrial Flutter (n=4,051). ResNet deep learning algorithm for RR interval-based arrhythmia detection was evaluated on Arrhythmia detection accuracy (Arrhythmia vs. Non-arrhythmia) (Accuracy 99.98%, Sensitivity 100.00%, Specificity 99.94%). A ResNet deep learning algorithm using extracted RR interval signals achieved an overall diagnostic accuracy of 99.98% for detecting arrhythmias (AFIB and AFL).
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