Identification of atrial sites that perpetuate atrial fibrillation (AF), and ablation thereof terminates AF, is challenging. We hypothesized that specific electrogram (EGM) characteristics identify AF-termination sites (AFTS). Twenty-one patients in whom low-voltage-guided ablation after pulmonary vein isolation terminated clinical persistent AF were included. Patients were included if short RF-delivery for <8sec at a given atrial site was associated with acute termination of clinical persistent AF. EGM-characteristics at 21 AFTS, 105 targeted sites without termination and 105 non-targeted control sites were analyzed. Alteration of EGM-characteristics by local fibrosis was evaluated in a three-dimensional high resolution (100 µm)-computational AF model. AFTS demonstrated lower EGM-voltage, higher EGM-cycle-length-coverage, shorter AF-cycle-length and higher pattern consistency than control sites (0.49 ± 0.39 mV vs. 0.83 ± 0.76 mV, p < 0.0001; 79 ± 16% vs. 59 ± 22%, p = 0.0022; 173 ± 49 ms vs. 198 ± 34 ms, p = 0.047; 80% vs. 30%, p < 0.01). Among targeted sites, AFTS had higher EGM-cycle-length coverage, shorter local AF-cycle-length and higher pattern consistency than targeted sites without AF-termination (79 ± 16% vs. 63 ± 23%, p = 0.02; 173 ± 49 ms vs. 210 ± 44 ms, p = 0.002; 80% vs. 40%, p = 0.01). Low voltage (0.52 ± 0.3 mV) fractionated EGMs (79 ± 24 ms) with delayed components in sinus rhythm ('atrial late potentials', respectively 'ALP') were observed at 71% of AFTS. EGMs recorded from fibrotic areas in computational models demonstrated comparable EGM-characteristics both in simulated AF and sinus rhythm. AFTS may therefore be identified by locally consistent, fractionated low-voltage EGMs with high cycle-length-coverage and rapid activity in AF, with low-voltage, fractionated EGMs with delayed components/ 'atrial late potentials' (ALP) persisting in sinus rhythm.
Aims Atrial cardiomyopathy (ACM) is associated with new-onset atrial fibrillation, arrhythmia recurrence after pulmonary vein isolation (PVI) and increased risk for stroke. At present, diagnosis of ACM is feasible by endocardial contact mapping of left atrial (LA) low-voltage substrate (LVS) or late gadolinium-enhanced magnetic resonance imaging, but their complexity limits a widespread use. The aim of this study was to assess non-invasive body surface electrocardiographic imaging (ECGI) as a novel clinical tool for diagnosis of ACM compared with endocardial mapping. Methods and results Thirty-nine consecutive patients (66 ± 9 years, 85% male) presenting for their first PVI for persistent atrial fibrillation underwent ECGI in sinus rhythm using a 252-electrode-array mapping system. Subsequently, high-density LA voltage and biatrial activation maps (mean 2090 ± 488 sites) were acquired in sinus rhythm prior to PVI. Freedom from arrhythmia recurrence was assessed within 12 months follow-up. Increased duration of total atrial conduction time (TACT) in ECGI was associated with both increased atrial activation time and extent of LA-LVS in endocardial contact mapping (r = 0.77 and r = 0.66, P < 0.0001 respectively). Atrial cardiomyopathy was found in 23 (59%) patients. A TACT value of 148 ms identified ACM with 91.3% sensitivity and 93.7% specificity. Arrhythmia recurrence occurred in 15 (38%) patients during a follow-up of 389 ± 55 days. Freedom from arrhythmia was significantly higher in patients with a TACT <148 ms compared with patients with a TACT ≥148 ms (82.4% vs. 45.5%, P = 0.019). Conclusion Analysis of TACT in non-invasive ECGI allows diagnosis of patients with ACM, which is associated with a significantly increased risk for arrhythmia recurrence following PVI.
Background: Rate-varying S1S2 stimulation protocols can be used for restitution studies to characterize atrial substrate, ionic remodeling, and atrial fibrillation risk. Clinical restitution studies with numerous patients create large amounts of these data. Thus, an automated pipeline to evaluate clinically acquired S1S2 stimulation protocol data necessitates consistent, robust, reproducible, and precise evaluation of local activation times, electrogram amplitude, and conduction velocity. Here, we present the CVAR-Seg pipeline, developed focusing on three challenges: (i) No previous knowledge of the stimulation parameters is available, thus, arbitrary protocols are supported. (ii) The pipeline remains robust under different noise conditions. (iii) The pipeline supports segmentation of atrial activities in close temporal proximity to the stimulation artifact, which is challenging due to larger amplitude and slope of the stimulus compared to the atrial activity. Methods and Results: The S1 basic cycle length was estimated by time interval detection. Stimulation time windows were segmented by detecting synchronous peaks in different channels surpassing an amplitude threshold and identifying time intervals between detected stimuli. Elimination of the stimulation artifact by a matched filter allowed detection of local activation times in temporal proximity. A non-linear signal energy operator was used to segment periods of atrial activity. Geodesic and Euclidean inter electrode distances allowed approximation of conduction velocity. The automatic segmentation performance of the CVAR-Seg pipeline was evaluated on 37 synthetic datasets with decreasing signal-to-noise ratios. Noise was modeled by reconstructing the frequency spectrum of clinical noise. The pipeline retained a median local activation time error below a single sample (1 ms) for signal-to-noise ratios as low as 0 dB representing a high clinical noise level. As a proof of concept, the pipeline was tested on a CARTO case of a paroxysmal atrial fibrillation patient and yielded plausible restitution curves for conduction speed and amplitude. Conclusion: The proposed openly available CVAR-Seg pipeline promises fast, fully automated, robust, and accurate evaluations of atrial signals even with low signal-to-noise ratios. This is achieved by solving the proximity problem of stimulation and atrial activity to enable standardized evaluation without introducing human bias for large data sets.
Objectives: This study hypothesized that P-wave morphology and timing under left atrial appendage (LAA) pacing change characteristically immediately upon anterior mitral line (AML) block. Background: Perimitral flutter commonly occurs following ablation of atrial fibrillation and can be cured by an AML. However, confirmation of bidirectional block can be challenging, especially in severely fibrotic atria. Methods: The study analyzed 129 consecutive patients (66 ± 8 years, 64% men) who developed perimitral flutter after atrial fibrillation ablation. We designed electrocardiography criteria in a retrospective cohort (n = 76) and analyzed them in a validation cohort (n = 53). Results: Bidirectional AML block was achieved in 110 (85%) patients. For ablation performed during LAA pacing without flutter (n = 52), we found a characteristic immediate V1 jump (increase in LAA stimulus to P-wave peak interval in lead V1) as a real-time marker of AML block (V1 jump ≥30 ms: sensitivity 95%, specificity 100%, positive predictive value 100%, negative predictive value 88%). As V1 jump is not applicable when block coincides with termination of flutter, absolute V1 delay was used as a criterion applicable in all cases (n = 129) with a delay of 203 ms indicating successful block (sensitivity 92%, specificity 84%, positive predictive value 90%, negative predictive value 87%). Furthermore, an initial negative P-wave portion in the inferior leads was observed, which was attenuated in case of additional cavotricuspid isthmus ablation. Computational P-wave simulations provide mechanistic confirmation of these findings for diverse ablation scenarios (pulmonary vein isolation ± AML ± roof line ± cavotricuspid isthmus ablation). Conclusions: V1 jump and V1 delay are novel real-time electrocardiography criteria allowing fast and straightforward assessment of AML block during ablation for perimitral flutter.
BACKGROUND AND OBJECTIVE: Progress in biomedical engineering has improved the hardware available for diagnosis and treatment of cardiac arrhythmias. But although huge amounts of intracardiac electrograms (EGMs) can be acquired during electrophysiological examinations, there is still a lack of software aiding diagnosis. The development of novel algorithms for the automated analysis of EGMs has proven difficult, due to the highly interdisciplinary nature of this task and hampered data access in clinical systems. Thus we developed a software platform, which allows rapid implementation of new algorithms, verification of their functionality and suitable visualization for discussion in the clinical environment. METHODS: A software for visualization was developed in Qt5 and C++ utilizing the class library of VTK. The algorithms for signal analysis were implemented in MATLAB. Clinical data for analysis was exported from electroanatomical mapping systems. RESULTS: The visualization software KaPAVIE (Karlsruhe Platform for Analysis and Visualization of Intracardiac Electrograms) was implemented and tested on several clinical datasets. Both common and novel algorithms were implemented which address important clinical questions in diagnosis of different arrhythmias. It proved useful in discussions with clinicians due to its interactive and user-friendly design. Time after export from the clinical mapping system to visualization is below 5min. CONCLUSION: KaPAVIE(2) is a powerful platform for the development of novel algorithms in the clinical environment. Simultaneous and interactive visualization of measured EGM data and the results of analysis will aid diagnosis and help understanding the underlying mechanisms of complex arrhythmias like atrial fibrillation.
Clinical and computational studies highlighted the role of atrial anatomy for atrial fibrillation vulnerability. However, personalized computational models are often generated from electroanatomical maps, which might lack important anatomical structures like the appendages, or from imaging data which are potentially affected by segmentation uncertainty. A bi-atrial statistical shape model (SSM) covering relevant structures for electrophysiological simulations was shown to cover atrial shape variability. We hypothesized that it could, therefore, also be used to infer the shape of missing structures and deliver ready-to-use models to assess atrial fibrillation vulnerability in silico. We implemented a highly automatized pipeline to generate a personalized computational model by fitting the SSM to the clinically acquired geometries. We applied our framework to a geometry coming from an electroanatomical map and one derived from magnetic resonance images (MRI). Only landmarks belonging to the left atrium and no information from the right atrium were used in the fitting process. The left atrium surface-to-surface distance between electroanatomical map and a fitted instance of the SSM was 2.26+-1.95 mm. The distance between MRI segmentation and SSM was 2.07+-1.56 mm and 3.59+-2.84 mm in the left and right atrium, respectively. Our semi-automatic pipeline provides ready-to-use personalized computational models representing the original anatomy well by fitting a SSM. We were able to infer the shape of the right atrium even in the case of using information only from the left atrium.
Catheter ablation targeting low voltage areas (LVA) is commonly being used to treat atrial fibrillation (AF) in pa- tients with persistent AF. However, it is not always certain that the areas marked as low voltage (LV) are correct. This can be related to how the voltage is calculated. There- fore, this paper focuses on comparing different calculation methods, specifically, with regards to spatial distribution. Two voltage maps obtained in AF were used, removing points which did not meet the required specifications. The peaks for the remaining points, in regions of the left atrium, were then found and the voltage was calculated based on taking the peak to peak (p2p) for different beats. For around 30% of the points on the map, the voltage only changed by 0.1mV when taking one beat versus all beats. However, for some individual points, the difference was substantial, around 0.8mV, depending on the beat cho- sen. Additionally, the inter-method variability increased by around 0.1mV when considering all methods compared to only methods calculated using more than one point. It was found that taking a method which considers all p2p values would be a more appropriate method for cal- culating the voltage. Thus, providing a technique, which could improve the accuracy of identifying LVA in an AF map.
Background: Perimitral flutter commonly occurs following ablation of atrial fibrillation (AF) and can be cured by an anterior mitral line (AML). However, confirmation of bidirectional block can be challenging. Objective: We hypothesized that P-wave morphology and timing under left atrial appendage (LAA) pacing changes upon AML- block. Methods: We analyzed 129 consecutive patients (66±8 y, 64%male) who developed perimitral flutter after AF ablation. We designed ECG-criteria in a retrospective cohort (n=76) and analyzed them in a validation cohort (n=53). Results: Bidirectional AML-block was achieved in 110 patients (85%). For ablation performed during LAA-pacing without flutter (n=52), we found an immediate V1-jump (increase in LAA- stimulus to P-wave peak in lead V1) as a real-time marker of AML-block (V1-jump ≥30ms: sensitivity 95%, specificity 100%, PPV 100%, NPV 88%). Since V1-jump is not applicable when block coincides with termination of flutter, absolute V1-delay was used as a criterion applicable in all cases (n=129) with a delay of 203ms indicating block (sensitivity 92%, specificity 84%, PPV 90%, NPV 87%). Furthermore, an initial negative P-wave portion in the inferior leads was observed, which was attenuated in case of additional cavotricuspid isthmus (CTI) ablation. Computational P-wave simulations provide mechanistic confirmation of these findings for diverse ablation scenarios (pulmonary vein isolation±AML±roof-line±CTI ablation). Conclusion: V1-jump and V1-delay are novel real-time ECG- criteria allowing fast and straightforward assessment of AML- block during ablation for perimitral flutter.
The arrhythmogenic mechanisms of atrial fibrillation (AF) are still not well understood. Increased atrial fibrosis is a structural hallmark in patients with persistent AF. We assessed the electrogram signature rotational activity and their spatial relationship to low voltage areas in patients with persistent AF. Computer simulations implicating 3- dimensional atrial tissue with different amount of atrial fibrosis were used to assess development and stability of rotational activities during AF. Rotor anchoring occurred at the borderzone between fibrosis and healthy atrial tissue with 12 consecutive rotations prior to rotor extinction. Rotational activity in fibrotic tissue resulted in fractionated signals and were overlapped with large negative electrograms in unipolar recording mode from neighboring healthy tissue impressing as a focal source. Necessary conditions for development and stability of rotational activities around fibrosis were on the one hand a minimum size of atrial fibrosis area equal or larger than 10mm x 10mm and on the other hand the degree of atrial fibrosis of 40%. Clinical data showed that AF termination sites were located within low voltage areas (displaying <0,5mV in AF on the multielectrode mapping catheter) in 80% and at their borderzones in 20% of cases.
Atrial fibrillation is a common irregular heart rhythm. Until today there is still a need for research to quantify typical signal characteristics of rotors, which can induce atrial fibrillation. In this work, signal characteristics of a stable and a more unstable rotor in a realistic heart model including fiber orientation were analyzed with the following methods: peak-to-peak amplitude, Hilbert phase, approximate entropy and RS-difference. In this simulation model the stable rotor rotated with a cycle length of 145 ms and stayed in an area of 1.5 mm x 3 mm. Another more unstable rotor with a cycle length of 190 ms moved in an area of 10 mm × 4 mm. In a distance of 2 mm to the rotor tip, the peak-to-peak amplitude decreased significantly, whereas the RS-difference and the approximate entropy were maximal. The rotor center trajectories were detected by phase singularity points determined by the Hilbert transform. We showed that more unstable rotors resulted in more amplitude changes over time and also the cycle length differed more. Furthermore, we presented typical activation time patterns of the Lasso catheter centered at the rotor tip and in different distances to the rotor tip. We suggest that cardiologists use a combination of the described methods to determine a rotor tip position in a more robust manner.