A cycle length iteration algorithm for detecting atrial activations during atrial fibrillation achieved a total discrepancy rate of 7.0%, outperforming dominant frequency and fixed threshold methods.
Atrial fibrillation (AF) electrograms are characterized by varying morphologies, amplitudes, and cycle lengths (CLs), presenting a challenge for automated detection of individual activations and the activation rate. In this study, we evaluate an algorithm to detect activations and measure CLs from AF electrograms. This algorithm iteratively adjusts the detection threshold level until the mean CL converges with the median CL to detect all individual activations. A total of 291 AF electrogram recordings from 13 patients (11 male, 58 ± 10 years old) undergoing AF ablation were obtained. Using manual markings by two independent reviewers as the standard, we compared the cycle length iteration algorithm with a fixed threshold algorithm and dominant frequency (DF) for the estimation of CL. At segment lengths of 10 s, when comparing the algorithm detected to the manually detected activation, the undersensing, oversensing, and total discrepancy rates were 2.4%, 4.6%, and 7.0%, respectively, and with absolute differences in mean and median CLs were 7.9 ± 9.6 ms and 5.6 ± 6.8 ms, respectively. These results outperformed DF and fixed threshold-based measurements. This robust method can be used for CL measurements in either real-time and offline settings and may be useful in the mapping of AF.
Ng et al. (Tue,) conducted a other in Atrial fibrillation (n=13). Cycle length iteration algorithm vs. Fixed threshold algorithm and dominant frequency (DF) was evaluated on Total discrepancy rate compared to manual markings. A cycle length iteration algorithm for detecting atrial activations during atrial fibrillation achieved a total discrepancy rate of 7.0%, outperforming dominant frequency and fixed threshold methods.
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