Key result
Adding pRR30 and asymmetric entropy offers negligible improvement over pRR3.25% alone for detecting AF.
Why the study?
Early detection of AF is essential to prevent stroke and other complications, motivating comparison of pRR30, pRR3.25%, and asymmetric entropy descriptors for AF detection.
Does the addition of asymmetric entropy descriptors to pRR30 and pRR3.25% improve the detection of atrial fibrillation in 60-second ECG segments?
Observational (n=84)
Does the addition of asymmetric entropy descriptors to pRR30 and pRR3.25% improve the detection of atrial fibrillation in 60-second ECG segments?
Absolute Event Rate: 0.98% vs 0.972%
Adding asymmetric entropy descriptors to pRR30 and pRR3.25% does not provide a clinically relevant improvement in detecting atrial fibrillation from 60-second ECG segments and may increase false negatives.
Adding entropy yields negligible gain over pRR3.25% alone for short-ECG AF detection; leaves open whether complex models add value beyond simple metrics.
Background: Early detection of atrial fibrillation (AF) is essential to prevent stroke and other cardiac and embolic complications. We compared the diagnostic properties for AF detection of the percentage of successive RR interval differences greater than or equal to 30 ms or 3.25% of the previous RR interval (pRR30 and pRR3.25%, respectively), and asymmetric entropy descriptors of RR intervals. Previously, both pRR30 and pRR3.25% outperformed many other heart rate variability (HRV) parameters in distinguishing AF from sinus rhythm (SR) in 60-second electrocardiograms (ECGs). Methods: The 60-s segments with RR intervals were extracted from the publicly available Physionet Long-Term Atrial Fibrillation Database (84 recording, 24-hour Holter ECG). There were 31753 60-s segments of AF and 32073 60-s segments of SR. The diagnostic properties of all parameters were analysed with the receiver operator curve analysis, confusion matrix and logistic regression. The best model with pRR30, pRR3.25% and total entropic features (H) had the largest area under the curve (AUC) – 0.98 compared to 0.959 for pRR30 - and 0.972 for pRR3.25%. However, the differences in AUC between pRR30, pRR3.25% alone and the combined model were negligible from a practical point of view. Moreover, combining pRR30, pRR3.25% with H significantly increased the number of false-negative cases by more than threefold. Conclusions: Asymmetric entropy has some potential in differentiating AF from SR in the 60-s RR interval time series, but the addition of these parameters does not seem to make a relevant difference compared to pRR30 and especially pRR3.25%.
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Biczuk et al. (2024) conducted an observational in Atrial Fibrillation (n=84). Combined model of pRR30, pRR3.25% and asymmetric entropy (H) vs. pRR30 and pRR3.25% alone was evaluated on Area under the curve (AUC) for distinguishing AF from sinus rhythm. A combined model of pRR30, pRR3.25%, and asymmetric entropy detected atrial fibrillation with an AUC of 0.98, offering negligible practical improvement over pRR3.25% alone (AUC 0.972).
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