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.

Objective This study sought to test the hypothesis that “virtual” electrophysiologic studies (EPS) on an anatomic platform generated by 3D MRI reconstruction of the left ventricle (LV) can reproduce the reentrant circuits of induced ventricular tachycardia (VT) in a porcine model of myocardial infarction (MI). Background Delayed-enhancement MRI has been used to characterize MI and “gray zones”, which are thought to reflect heterogeneous regions of viable and non-viable myocytes. Methods MI by coronary artery occlusion was induced in eight pigs. After a recovery period, 3D cardiac MRIs were obtained from each pig in-vivo. Normal areas, gray zones, and infarct cores were classified based on voxel intensity. In the computer model, gray zones were assigned slower conduction and longer action potential durations than those for normal myocardium. Virtual EPS was performed and was compared to results of actual in vivo programmed stimulation and non-contact mapping. Results The LV volumes ranged from 97.8 to 166.2 cm3 with 4.9 to 17.5% of voxels classified as infarct zones. Six of the seven pigs that developed VT during actual EPS were also inducible with virtual EPS. Four of the six pigs that had simulated VT had reentrant circuits that approximated the circuits seen with non-contact mapping, while the remaining two had similar circuits but propagating in opposite directions. Conclusions This initial study demonstrates the feasibility of applying a mathematical model to MRI reconstructions of the LV to predict VT circuits. Virtual EPS may be helpful to plan catheter ablation strategies or to identify patients who are at risk for future episodes of VT.

Paroxysmal atrial fibrillation (AF) is self-terminating by definition, but the mechanisms by which this occurs are not wall understood. Holter recordings were used to develop and test algorithms for distinguishing between AF segments that are Non-terminating (N), Terminating within a second (T), and Soon terminating (within a minute, S). From rhe training set, the peak frequency ranges (mean ?SDI were 4.8-6.0 (5.3-q.4) Hz for T 4.7-6.4 (5.230.6) Hz for S, 4.8-7.3 (6.51yI.8) Hz for N. In 8 of IO T recordings there was a decrease in peak frequency from the penultimate to the ultimare second and in 8 of 10 T recordings there was a decrease in peak power. The last second had a lower peak frequency for T when compared to S of the same patient in 9 of I O patients.

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