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Dominant frequency (DF) computed from action potentials is a key parameter for investigating atrial fibrillation in animal studies and computer models. A recent clinical trial reported consistent results computing DF from 30 Hz to 400 Hz bandpass filtered bipolar electrograms in humans. The DF (< 15 Hz and, thus, filtered out) was recovered by rectifying the signal, while the theoretical background of this approach was left uncommented. It is the focus of this paper to provide this background by a Fourier analysis. We demonstrate that it is mainly the timing of the narrow deflections (local activation at the catheter tip) which contribute to the DF peak in the frequency spectrum. Due to the typical signal morphology pronounced harmonic peaks occur in the spectrum. This is a disadvantage when computing the regularity index (RI) as a parameter for local organization and signal quality. It is demonstrated for synthetical and patient data that at low DF the RI is far below the optimal value one even for high underlying organization and good signal quality. The insight obtained promotes the development of better measures for organization. The finding that mainly timing of activation contributes to DF might promote the development of powerful realtime signal processing tools for computing DF.

Objectives: To evaluate the long-term performance of the SonRtip atrial lead. Background: To optimize atrioventricular and interventricular timing and thereby potentially improving cardiac resynchronization therapy (CRT) responder rates, a lead integrated technology and a cardioverter/defibrillator-based algorithm measuring peak endocardial acceleration have been introduced. Long-term performance of the atrial lead (SonRtip PS55D, Sorin/MicroPort CRM, Italy) embedded with such a sensor has not been reported so far. Methods: Between 2012 and 2018, 143 patients underwent implantation of the SonRtip atrial lead in four Austrian medical centers. Conventional bipolar atrial leads implanted during the same period in 526 patients receiving CRT were used as control cohort. Results: Among 669 patients included in the study, 10 (1.5%) showed increased atrial pacing thresholds and/or decreased atrial sensing amplitudes and/or sudden increase in atrial lead impedance (above 3000 Ω) after an uneventful early postoperative period. Seven (70%) of the malfunctioning leads were SonRtip leads (p <0.001). Lead replacement was needed in 4.2% of SonRtip leads (six out of 143) and in 0.38% of all other conventional atrial leads (two out of 526) (p <0.001). Because of unaltered atrial sensing properties, a wait and see strategy was chosen in two patients–one of them with a SonRtip lead. The implanted atrial lead in the latter person experienced a sudden increase in pacing threshold (4V/0.35ms). Conclusions: While short-term safety and stable technical performance of the SonRtip atrial lead could be confirmed, our study found an unexpectedly high malfunction rate over a longer follow-up period. [ABSTRACT FROM AUTHOR]

(1) Background: The exact anatomic localization of the accessory pathway (AP) in patients with Wolff–Parkinson–White (WPW) syndrome still relies on an invasive electrophysiologic study, which has its own inherent risks. Determining the AP localization using a 12-lead ECG circumvents this risk but is of limited diagnostic accuracy. We developed and validated an artificial intelligence-based algorithm (location of accessory pathway artificial intelligence (locAP AI)) using a neural network to identify the AP location in WPW syndrome patients based on the delta-wave polarity in the 12-lead ECG. (2) Methods: The study included 357 consecutive WPW syndrome patients who underwent successful catheter ablation at our institution. Delta-wave polarity was assessed by four independent electrophysiologists, unaware of the site of successful catheter ablation. LocAP AI was trained and internally validated in 357 patients to identify the correct AP location among 14 possible locations. The AP location was also determined using three established tree-based, ECG-based algorithms (Arruda, Milstein, and Fitzpatrick), which provide limited resolutions of 10, 5, and 8 AP locations, respectively. (3) Results: LocAP AI identified the correct AP location with an accuracy of 85.7% (95% CI 79.6–90.5, p < 0.0001). The algorithms by Arruda, Milstein, and Fitzpatrick yielded a predictive accuracy of 53.2%, 65.6%, and 44.7%, respectively. At comparable resolutions, the locAP AI achieved a predictive accuracy of 95.0%, 94.9%, and 95.6%, respectively (p < 0.001 for differences). (4) Conclusions: Our AI-based algorithm provided excellent accuracy in predicting the correct AP location. Remarkably, this accuracy is achieved at an even higher resolution of possible anatomical locations compared to established tree-based algorithms. [ABSTRACT FROM AUTHOR]

HINTRINGER, F., et al.: Comparison of the Specificity of Implantable Dual Chamber Defibrillator Detection Algorithms. The aim of the study was to compare the specificity of dual chamber ICDs detection algorithms for correct classification of supraventricular tachyarrhythmias derived from clinical studies according to their size to detect an impact of sample size on the specificity. Furthermore, the study sought to compare the specificities of detection algorithms calculated from clinical data with the specificity calculated from simulations of tachyarrhythmias. A survey was conducted of all available sources providing data regarding the specificity of five dual chamber ICDs. The specificity was correlated with the number of patients included, number of episodes, and number of supraventricular tachyarrhythmias recorded. The simulation was performed using tachyarrhythmias recorded in the electrophysiology laboratory. The range of the number of patients included into the studies was 78–1,029, the range of the total number of episodes recorded was 362–5,788, and the range of the number of supraventricular tachyarrhythmias used for calculation of the specificity for correct detection of these arrhythmias was 100 (Biotronik) to 1662 (Medtronic). The specificity for correct detection of supraventricular tachyarrhythmias was 90% (Biotronik), 89% (ELA Medical), 89% (Guidant), 68% (Medtronic), and 76% (St. Jude Medical). There was an inverse correlation (r =−0.9, P = 0.037) between the specificity for correct classification of supraventricular tachyarrhythmias and the number of patients. The specificity for correct detection of supraventricular tachyarrhythmias calculated from the simulation after correction for the clinical prevalence of the simulated tachyarrhythmias was 95% (Biotronik), 99% (ELA Medical), 94% (Guidant), 93% (Medtronic), and 92% (St. Jude Medical). In conclusion, the specificity of ICD detection algorithms calculated from clinical studies or registries may depend on the number of patients studied. Therefore, a direct comparison between different detection algorithms based on clinical data is difficult. In contrast, simulation of supraventricular tachyarrhythmias using a uniform database may be a better tool for direct comparison of the specificity of ICD detection algorithms. (PACE 2004; 27:976–982) [ABSTRACT FROM AUTHOR]