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This work proposes a novel deep learning based method for the identification of periodic patterns in Electrocardiographic Imaging (ECGI) signals and demonstrates its ability to identify, quantify, and visualize recurring patterns. ECGIs from AF patients obtained prior to pulmonary vein isolation (PVI) are encoded to a lower-dimensional feature space using a 3D-CNN autoencoder, and further processed with principal component analysis to aggregate recurring patterns and quantify their contribution to the overall spatiotemporal propagation pattern. Several markers are evaluated as potential predictors of AF recurrence. The variance captured by the first 3 principal components (PCs) varied from 19.8% to 59.2% (32.2±9.35) in different patients showed an inter-segment correlation exceeding 64%. Similarly, the number of PCs necessary to explain 90% of variance in ECGI recordings varied from 20 to 90 (56.2±20.1) demonstrating a varying number of propagation patterns across patients, which was reproducible intra-patient with an inter-segment correlation higher than 69%. In addition, backpropagation-based saliency maps are computed to identify in which atrial regions the captured patterns occur. Saliency maps are visualized on 3D atrial models to aid in the interpretation and anatomical contextualization

[EN] Electrocardiographic imaging (ECGI) is a technique to reconstruct non-invasively the electrical activity on the heart surface from body-surface potential recordings and geometric information of the torso and the heart. ECGI has shown scientific and clinical value when used to characterize and treat both atrial and ventricular arrhythmias. Regarding atrial fibrillation (AF), the characterization of the electrical propagation and the underlying substrate favoring AF is inherently more challenging than for ventricular arrhythmias, due to the progressive and heterogeneous nature of the disease and its manifestation, the small volume and wall thickness of the atria, and the relatively large role of microstructural abnormalities in AF. At the same time, ECGI has the advantage over other mapping technologies of allowing a global characterization of atrial electrical activity at every atrial beat and non-invasively. However, since ECGI is time-consuming and costly and the use of electrical mapping to guide AF ablation is still not fully established, the clinical value of ECGI for AF is still under assessment. Nonetheless, AF is known to be the manifestation of a complex interaction between electrical and structural abnormalities and therefore, true electro-anatomical-structural imaging may elucidate important key factors of AF development, progression, and treatment. Therefore, it is paramount to identify which clinical questions could be successfully addressed by ECGI when it comes to AF characterization and treatment, and which questions may be beyond its technical limitations. In this manuscript we review the questions that researchers have tried to address on the use of ECGI for AF characterization and treatment guidance (for example, localization of AF triggers and sustaining mechanisms), and we discuss the technological requirements and validation. We address experimental and clinical results, limitations, and future challenges for fruitful application of ECGI for AF understanding and management. We pay attention to existing techniques and clinical application, to computer models and (animal or human) experiments, to challenges of methodological and clinical validation. The overall objective of the study is to provide a consensus on valuable directions that ECGI research may take to provide future improvements in AF characterization and treatment guidance., JS was supported by Grants #2020/12841-3, #2020/13017-2, and #2018/25606-2, Sao Paulo Research Foundation (FAPESP). FS was supported by research grants from the Medical Research Council UK (MR/S037306/1), and from the British Heart Foundation (BHF Project Grant No. PG/18/33/33780). JR-A was supported by the projects (PID2019-106623RBC41) and meHeart (PID2019-104356RB-C42 and PID2019104356RB-C43) from the Spanish Government. JP was funded by Program of Alliances for Education and Training (Scholarship Brazil-PAEC OEA-GCUB-2017). RM, AC, and MG were supported by the Instituto de Salud Carlos III, and Ministerio de Ciencia, Innovacion y Universidades (supported by the FEDER Fondo Europeo de Desarrollo Regional PI17/01106 and RYC2018-024346B-750), EIT Health (Activity code 19600, EIT Health was supported by the EIT, a body of the European Union), received funding from the European Union's Horizon research and Innovation programme under the Marie Sklodowska-Curie grant agreement no. 860974 and the Generalitat Valenciana Grants (ACIF/2020/265). BZ and RM were supported by the NIH NHLBI Grant No. 1F30HL149327; NIH NIGMS Center for Integrative Biomedical Computing (www.sci.utah.edu/cibc), NIH NIGMS Grants P41GM103545 and R24 GM136986; and the Nora Eccles Treadwell Foundation for Cardiovascular Research. OB was supported in part by research grants from the National Institutes of Health (R01-HL118304 and R21-HL153694), Abbott, Medtronic, and CoreMap. MC was supported by a Veni Grant from the Netherlands Organization for Scientific Research (TTW 16772). FA was supported by the Instituto de Salud Carlos III (PI17/010559 and PI20/01618) and the Consorcio de Investigacion Biomedica en Red de Enfermedades Cardiovasculares (CIBERCV), the Ministerio de Ciencia e Innovacion (cofound by FEDER: Fondo Europeo de Desarrollo Regional), EIT Health (AFFINE 19600, EIT Health was supported by EIT, a body of the European Union).

[EN] Atrial fibrillation (AF) is characterized by complex and irregular propagation patterns. Noninvasive electrocardiographic imaging (ECGI) has been tested during AF conditions with promising results. However, current regularization methods face important challenges in this type of unstable electrical activity scenarios. Combination of intracardiac and non-invasive simultaneous recordings could improve ECGI performance and allow real-time global mapping of complex AF patterns. In this work, we propose an ECGI method that incorporates intracardiac measurements as a constraint in a reformulation of the classical Tikhonov method. We used realistic mathematical models of atria and torso that simulates a wide number of epicardial electrical activity patterns. Body surface potentials were obtained from simulated electrograms (EGMs) by using Boundary Element Method and corrupted with Gaussian noise. Epicardial potentials were estimated using inverse problem with Tikhonov regularization, including intracavitary information as a second constraint. Results showed that first-order Constrained Tikhonov formulation provided more reliable reconstructions than the classical Tikhonov approach in AF conditions using at least 32 uniformly distributed endocardial EGMs (CC between 0.87 and 0.28, depending on the AF complexity). Constrained Tikhonov provided more accurate spatial mass functions (SMF) of PS locations (CC smF between 0.24 and 0.86). This methodology was tested on real patient data, obtaining a mean DF RMSE of 0.85 Hz, outperforming the classical Tikhonov approach. Limitations of this study include the fact that the model considered endocardium and epicardium as a single layer. Further research will include endocardium-epicardium bilayer model approximations and validation using more real patient data., This work has been partially supported by projects TEC2016-75361R and PID2019-105032GB-I00 from the Spanish Ministry of Economy and Spanish Ministry of Science and Innovation, respectively, projects IJCI-2014-22178, PI16-01123, DTS16/00160, PI17/0159, PI17/01106 from the Spanish Ministry of Economy through the Carlos III Health Institute with FEDER founds, grant GVA APOSTD/2017/068 and projects AICO/2018/267, GV/2018/103 from the Education, Research, Culture and Sports department of Generalitat Valenciana, Spain.

[EN] Developed and under-developed countries are facing several challenges related to public health and sustainability of health care systems. New challenges demand of the collaborative action of multiple stakeholders with different backgrounds. In the late years, telecommunication engineers are involved in a wide range of companies and institutions to help designing and building innovative and efficient solutions, among which public health is a paradigmatic example. In this paper authors introduce a program for teaching public health principles and tools focused at telecommunications master students. The program is presented in five practices of three hours duration (fifteen hours overall). The sessions are structured in the classic problem-solving methodology in which the students must respond to concrete and general questions by the application of knowledge, practice and reasoning. Each practice includes theoretical framework introduction, provision of tools and use of open repositories to complete the assignments. The covered topics are: mobile health and usability, open data, data mining, Internet of Things and wearable and process mining.

[EN] Background: Electrocardiographic imaging (ECGI) allows evaluating the complexity of the reentrant activity of atrial fibrillation (AF) patients. In this study, we evaluated the ability of ECGI metrics to predict the success of pulmonary vein isolation (PVI) to treat AF. Methods: ECGI of 24 AF patients (6 males, 13 paroxysmal, 61.8 +/- 14 years) was recorded prior to PVI. Patients were distributed into two groups based on their PVI outcome 6 months after ablation (sinus vs. arrhythmia recurrence). Metrics derived from phase analysis of ECGI signals were computed for two different temporal segments before ablation. Correlation analysis and variability over time were studied between the two recorded segments and were compared between patient groups. Results: Temporal variability of both rotor duration and spatial entropy of the rotor histogram presented statistical differences between groups with different PVI outcome (p < 0.05). The reproducibility of reentrant metrics was higher (R-2 > 0.8) in patients with good outcome rather than arrhythmia recurrence patients (R-2 < 0.62). Prediction of PVI success based on ECGI temporal variability metrics allows for an increased specificity over the classification into paroxysmal or persistent (0.85 vs. 0.64). Conclusions: Patients with favorable PVI outcome present ECGI metrics more reproducible over time than patients with AF recurrence. These results suggest that ECGI derived metrics may allow selecting which patients would benefit from ablation therapies., This work was supported by: Instituto de Salud Carlos III, and Ministerio de Ciencia, Innovacion y Universidades (supported by FEDER Fondo Europeo de Desarrollo Regional PI17/01106 and RYC2018- 024346B-750), EIT Health (Activity code 19600, EIT Health is supported by EIT, a body of the European Union) and Generalitat Valenciana Conselleria d'Educacio, Investigacio, Cultura i Esport (ACIF/2020/265).

[EN] Background Multipoint pacing (MPP) in cardiac resynchronization therapy (CRT) activates the left ventricle from two locations, thereby shortening the QRS duration and enabling better resynchronization; however, compared with conventional CRT, MPP reduces battery longevity. On the other hand, electrocardiogram-based optimization using the fusion-optimized intervals (FOI) method achieves more significant reverse remodeling than nominal CRT programming. Our study aimed to determine whether MPP could attain better resynchronization than single-point pacing (SPP) optimized by FOI. Methods This prospective study included 32 consecutive patients who successfully received CRT devices with MPP capabilities. After implantation, the QRS duration was measured during intrinsic rhythm and with three pacing configurations: MPP, SPP-FOI, and MPP-FOI. In 14 patients, biventricular activation times (by electrocardiographic imaging, ECGI) were obtained during intrinsic rhythm and for each pacing configuration to validate the findings. Device battery longevity was estimated at the 45-day follow-up. Results The SPP-FOI method achieved greater QRS shortening than MPP (-56 +/- 16 vs. -42 +/- 17 ms, p < .001). Adding MPP to the best FOI programming did not result in further shortening (MPP-FOI: -58 +/- 14 ms, p = .69). Although biventricular activation times did not differ significantly among the three pacing configurations, only the two FOI configurations achieved significant shortening compared with intrinsic rhythm. The estimated battery longevity was longer with SPP than with MPP (8.1 +/- 2.3 vs. 6.3 +/- 2.0 years, p = .03). Conclusions SPP optimized by FOI resulted in better resynchronization and longer battery duration than MPP., Centro de Investigacion Biomedica en Red Enfermedades Cardiovasculares, Grant/Award Number: CB16/11/00354; Instituto de Salud Carlos III, Grant/Award Numbers: DTS16/0160, PI16/00435, PI16/00703, PI17/01059, PI17/01106; Sociedad Espanola de Cardiologia, Grant/Award Numbers: 2018, SEC_ESTIM_01; Agencia deGestio d'Ajuts Universitaris i de Recerca, Grant/AwardNumber: 2017_SGR_1548; Fundacio laMarato de TV3, Grant/Award Number: 20152730; Horizon 2020 Framework Programme, Grant/Award Number: 633196 - CATCH MEproject; European Regional Development Fund, Grant/Award Number: EITHealth 19600 AFFINE

Noninvasive electrocardiographic imaging (ECGi) is a useful tool that can be used to guide ablation procedures in atrial fibrillation (AF patients). Most ECGi resolutions are based on the Boundary Element Method, and thus application of Green's theorem, that requires that electrical sources reside inside a closed volume. The objective of this work is to quantify the error in atrial fibrillation metrics than can be expected if two volumes are segmented for the atria instead of just one. Our results show that segmenting the atria of the patients into two volumes instead of one does impact on rotor-related AF metrics that can be derived from ECGi whereas dominant-frequency metrics are less affected.

Phase mapping of inverse-computed electrograms (ECGI) is used for rotor activity identification to guide ablation procedures in atrial fibrillation (AF) patients. This study aims at identifying the post-processing settings that best allow tracking meaningful rotors. ECGI signals of 24 AF patients prior to pulmonary vein isolation (PVI) were recorded. Patients were divided according to their 6 months outcome (sinus vs. no sinus). Rotor metrics were compared using 3 types of processing: none, a narrow band-pass filtering centered at the highest dominant frequency (NB HDF), and sinusoidal recomposition filtering (SRC). Different thresholds for rotor duration were also compared (0.5, 1, and 1.5 turns). Differences in the absolute error of rotor metrics between sinus and no sinus patients were best identified with raw ECGI signals than filtered ECGI signals. In contrast, rotor duration threshold had little effect on the statistical differentiation between groups. Filtering of AF ECGI signals does not improve rotor identification to predict PVI outcome.

Atrial fibrillation (AF) is characterized by complex and irregular propagation patterns. Multipoint intracardiac mapping systems present a limited spatial resolution, which makes it difficult to identify AF drivers and ablation targets. These AF onset locations and drivers responsible for AF perpetuation are main targets for ablation procedures. Although noninvasive electrocardiographic imaging (ECGI) and inverse problem-based methods have been tested during AF conditions, they need an accurate mathematical modeling of atria and torso to get good results. In this work, we propose to model the location of AF drivers from body surface potentials (BPS) as a supervised classification problem. We used deep learning techniques to address the problem. We were able to correctly locate the 92% and 96% of drivers in the test and training sets, respectively (accuracy of 0.92 and 0.96), while the Cohen's Kappa was 0.89 for both sets. Therefore, proposed method can help to identify target regions for ablation using a noninvasive procedure as BSP mapping.

Atrial dominant frequency (DF) maps undergoing atrial fibrillation (AF) presented good spatial correlation with those obtained with the non-invasive body surface potential mapping (BSPM). In this study, a robust BSPM-DF calculation method based on wavelet analysis is proposed. Continuous wavelet transform (Gaussian mother wavelet) along 40 scales in the pseudo-frequency range of 3–30 Hz is performed in each BSPM signal. DFs are estimated from the intervals between the peaks, representing the activation times, in the maximum energy scale. The results are compared with the traditionally widely applied Welch periodogram. The accuracy of both methods was assessed using the absolute errors between BSPM and atrial highest DFs (HDFs) and assumed correct if ≤ 1. The robustness of the methods was tested on different protocols: increasing levels of WGN, artificial DF harmonics presence, and reduction of the number of leads. 11 AF simulations and 12 AF patients are considered in the analysis. The proposed method outperformed the Welch approach, obtaining more correct estimations of atrial HDFs non-invasively in models (81.82% vs 45.45%) and patients (75.00% vs 66.67%) and being more robust to noise and reduction in spatial resolution, thus helping to increase the non-invasive diagnostic ability of BSPM in AF.

Atrial tachycardia (AT), flutter (AFL) and fibrillation (AF) are very common cardiac arrhythmias and are driven by localized sources that can be ablation targets. Noninvasive body surface potential mapping (BSPM) can be useful for early diagnosis and ablation planning. We aimed to automatically classify and locate the arrhythmic mechanisms behind AT, AFL and AF using BSPM features. 19 simulations of 567-lead BSPMs were used to obtain dominant frequency (DF) maps from which features reflecting the spatial distribution of DFs and the spectral organization were extracted. Rotational activity was tracked based on singularity points in phase maps; features were extracted to reflect its spatio-temporal stability. The torso was divided in 4 quadrants to assess the spatial distribution of the features. Random forest and least-square based algorithms were used to classify the arrhythmias and their mechanisms' location, respectively. The analyses were reproduced in different layouts (252 to 12 leads). The arrhythmic mechanisms and their locations were classified with 72.0% and 73.9% balanced accuracy, respectively. Accuracy was similar along all lead layouts for arrhythmia classification but decreased for mechanism location. Classification of AT, AFL and AF and their mechanisms' location was feasible based on BSPM features reflecting their basic electrophysiological characteristics.

Background The cardiac late sodium current (INaL) has been increasingly implicated in the initiation of atrial fibrillation (AF). In fact, it has been reported that the augmentation of INaL in pathophysiological conditions prolongs repolarization and facilitates the appearance of afterdepolarizations, which can act as triggers of arrhythmic activity. Eleclazine is a novel selective inhibitor of INaL and is undergoing clinical testing for the treatment of cardiac arrhythmias. Purpose The aim of this study was to investigate the effects of eleclazine on spectral characteristics of atrial fibrillation in cultured atrial myocyte monolayer in order to assess whether this inhibitor could protect against cardiac arrhythmias. Methods Confluent HL-1 murine atrial myocyte monolayer with spontaneous fibrillatory activity was cultured in 1.5 cm diameter petri dishes (n=10). A high-resolution optical mapping system was used to record fibrillatory activity under basal conditions (without drug), and under eleclazine at increasing concentrations (1, 3 and 5 μM). Power spectra of optical signals were estimated by using Welch periodogram and dominant frequency (frequency with the largest peak in the spectrum between 0.05 and 30 Hz) was determined. The incidence of spontaneous defibrillation was analyzed under control and drug conditions. An ANOVA and a chi-squared test were used. Significance was reached when p Results Eleclazine at 1, 3 and 5 μM significantly decreased dominant frequency with respect to basal conditions (basal: 4.74±1.31 Hz; 1μM: 3.59±1.17 Hz, p Conclusions Selective late INa inhibition with eleclazine reduced dominant frequency of atrial fibrillation and facilitates the termination of arrhythmia in cultured atrial myocyte monolayer. Funding Acknowledgement Type of funding source: Public grant(s) – National budget only. Main funding source(s): Instituto de Salud Carlos III, Ministerio de Innovaciόn y Ciencia, Spain; Generalitat Valenciana, Spain

Background: Multipoint pacing (MPP) in cardiac resynchronization therapy (CRT) activates the left ventricle from two locations, thereby shortening the QRS duration and enabling better resynchronization; however, compared with conventional CRT, MPP reduces battery longevity. On the other hand, electrocardiogram-based optimization using the fusion-optimized intervals (FOI) method achieves more significant reverse remodeling than nominal CRT programming. Our study aimed to determine whether MPP could attain better resynchronization than single-point pacing (SPP) optimized by FOI. Methods: This prospective study included 32 consecutive patients who successfully received CRT devices with MPP capabilities. After implantation, the QRS duration was measured during intrinsic rhythm and with three pacing configurations: MPP, SPP-FOI, and MPP-FOI. In 14 patients, biventricular activation times (by electrocardiographic imaging, ECGI) were obtained during intrinsic rhythm and for each pacing configuration to validate the findings. Device battery longevity was estimated at the 45-day follow-up. Results: The SPP-FOI method achieved greater QRS shortening than MPP (-56±16 vs. -42±17 ms, P

[EN] Objective: Atrial dominant frequency (DF) maps undergoing atrial fibrillation (AF) presented good spatial correlation with those obtained with the non-invasive body surface potential mapping (BSPM). In this study, a robust BSPM-DF calculation method based on wavelet analysis is proposed. Approach: Continuous wavelet transform along 40 scales in the pseudo-frequency range of 3¿30 Hz is performed in each BSPM signal using a Gaussian mother wavelet. DFs are estimated from the intervals between the peaks, representing the activation times, in the maximum energy scale. The results are compared with the traditionally widely applied Welch periodogram and the robustness was tested on different protocols: increasing levels of white Gaussian noise, artificial DF harmonics presence and reduction in the number of leads. A total of 11 AF simulations and 12 AF patients are considered in the analysis. For each patient, intracardiac electrograms were acquired in 15 locations from both atria. The accuracy of both methods was assessed by calculating the absolute errors of the highest DFBSPM (HDFBSPM) with respect to the atrial HDF, either simulated or intracardially measured, and assumed correct if ¿1 Hz. The spatial distribution of the errors between torso DFs and atrial HDFs were compared with atria driving mechanism locations. Torso HDF regions, defined as portions of the maps with |DF ¿ HDFBSPM| ¿ 0.5 Hz were identified and the percentage of the torso occuping these regions was compared between methods. The robustness of both methods to white Gaussian noise, ventricular influence and harmonics, and to lower spatial resolution BSPM lead layouts was analyzed: computer AF models (567 leads vs 256 leads down to 16 leads) and patient data (67 leads vs 32 and 16 leads). Main results: The proposed method allowed an improvement in non-invasive estimation of the atria HDF. For the models the median relative errors were 7.14% for the wavelet-based algorithm vs 60.00% for the Welch method; in patients, the errors were 10.03% vs 12.66%, respectively. The wavelet method outperformed the Welch approach in correct estimations of atrial HDFs in models (81.82% vs 45.45%, respectively) and patients (66.67% vs 41.67%). A low positive BSPM-DF map correlation was seen between the techniques (0.47 for models and 0.63 for patients), highlighting the overall differences in DF distributions. The wavelet-based algorithm was more robust to white Gaussian noise, residual ventricular activity and harmonics, and presented more consistent results in lead layouts with low spatial resolution. Significance: Estimation of atrial HDFs using BSPM is improved by the proposed wavelet-based algorithm, helping to increase the non-invasive diagnostic ability in AF., This study was supported in part by grants from Sao Paulo Research Foundation (2017/19775-3), Instituto de Salud Carlos III FEDER (Fondo Europeo de Desarrollo Regional PI17/01106) and Generalitat Valenciana Grants (AICO/2018/267).

[EN] Background: It is difficult to noninvasively phenotype atrial fibrillation (AF) in a way that reflects clinical end points such as response to therapy. We set out to map electrical patterns of disorganization and regions of reentrant activity in AF from the body surface using electrocardiographic imaging, calibrated to panoramic intracardiac recordings and referenced to AF termination by ablation. Methods: Bi-atrial intracardiac electrograms of 47 patients with AF at ablation (30 persistent, 29 male, 63 +/- 9 years) were recorded with 64-pole basket catheters and simultaneous 57-lead body surface ECGs. Atrial epicardial electrical activity was reconstructed and organized sites were invasively and noninvasively tracked in 3-dimension using phase singularity. In a subset of 17 patients, sites of AF organization were targeted for ablation. Results: Body surface mapping showed greater AF organization near intracardially detected drivers than elsewhere, both in phase singularity density (2.3 +/- 2.1 versus 1.9 +/- 1.6; P=0.02) and number of drivers (3.2 +/- 2.3 versus 2.7 +/- 1.7; P=0.02). Complexity, defined as the number of stable AF reentrant sites, was concordant between noninvasive and invasive methods (r(2)=0.5; CC=0.71). In the subset receiving targeted ablation, AF complexity showed lower values in those in whom AF terminated than those in whom AF did not terminate (P, This article was supported in part by: Instituto de Salud Carlos III FEDER (Fondo Europeo de Desarrollo Regional; IJCI-2014-22178, DTS16/00160; PI14/00857, PI16/01123; PI17/01059; PI17/01106), Generalitat Valenciana Grants (APOSTD/2017 and APOSTD/2018) and projects (GVA/2018/103); National Institutes of Health (Dr Narayan: R01 HL85537; K24 HL103800); EITHealth 19600 AFFINE.

[EN] Identification of reentrant activity driving atrial fibrillation (AF) is increasingly important to ablative therapies. The goal of this work is to study how the automatically-classified quality of the electrograms (EGMs) affects reentrant AF driver localization. EGMs from 259 AF episodes obtained from 29 AF patients were recorded using 64-poles basket catheters and were manually classified according to their quality. An algorithm capable of identifying signal quality was developed using time and spectral domain parameters. Electrical reentries were identified in 3D phase maps using phase transform and were compared with those obtained with a 2D activation-based method. Effect of EGM quality was studied by discarding 3D phase reentries detected in regions with low-quality EGMs. Removal of reentries identified by 3D phase analysis in regions with low-quality EGMs improved its performance, increasing the area under the ROC curve (AUC) from 0.69 to 0.80. The EGMs quality classification algorithm showed an accurate performance for EGM classification (AUC 0.94) and reentry detection (AUC 0.80). Automatic classification of EGM quality based on time and spectral signal parameters is feasible and accurate, avoiding the manual labelling. Discard of reentries identified in regions with automatically-detected poor-quality EGMs improved the specificity of the 3D phase-based method for AF driver identification., Supported in part by: Instituto de Salud Carlos III FEDER (Fondo Europeo de Desarrollo Regional; IJCI-2014-22178, DTS16/00160; PI16/01123; PI17/01059; PI17/01106), Generalitat Valenciana Grants (APOSTD/2017 and APOSTD/2018) and projects (GVA/2018/103); National Institutes of Health (R01 HL85537; K24 HL103800); EIT-Health 19600 AFFINE; Nvidia Corporation (donation grants).

[EN] [Background] Identification of arrhythmogenic regions remains a challenge in persistent atrial fibrillation (persAF). Frequency and phase analysis allows identification of potential ablation targets. [Objective] This study aimed to investigate the spatiotemporal association between dominant frequency (DF) and reentrant phase activation areas. [Methods] A total of 8 persAF patients undergoing first-time catheter ablation procedure were enrolled. A noncontact array catheter was deployed into the left atrium (LA) and 2048 atrial fibrillation electrograms (AEGs) were acquired for 15 seconds following ventricular far-field cancellation. DF and phase singularity (PS) points were identified from the AEGs and tracked over consecutive frames. The spatiotemporal correlation of high DF areas and PS points was investigated, and the organization index at the core of high-DF areas was compared with that of their periphery. [Results] The phase maps presented multiple simultaneous PS points that drift over the LA, with preferential locations. Regions displaying higher PS concentration showed a degree of colocalization with DF sites, with PS and DF regions being neighbors in 61.8% and with PS and DF regions overlapping in 36.8% of the time windows. Sites with highest DF showed a greater degree of organization at their core compared with their periphery. After ablation, the PS incidence reduced over the entire LA (36.2% ± 23.2%, P < .05), but especially at the pulmonary veins (78.6% ± 22.2%, P < .05). [Conclusion] Multiple PS points drifting over the LA were identified with their clusters correlating spatially with the DF regions. After pulmonary vein isolation, the PS¿s complexity was reduced, which supports the notion that PS sites represent areas of relevance to the atrial substrate., This work was supported by the Leicester NIHR Cardiovascular Biomedical Research Unit, UK. Dr. Salinet was supported by Conselho Nacional de Desenvolvimento Cientifico e Tecnologico (CNPq, Brasil, N. 200598/2009-0) and Fundacao de Amparo a Pesquisa do Estado de Sao Paulo (FAPESP, Brasil, N. 2014/26066-0). Dr. Almeida was funded by Conselho Nacional de Desenvolvimento Cientifico e Tecnologico (CNPq, Brazil, N. 200251/2012-0) and Coordenacao de Aperfeicoamento de Pessoal de Nivel Superior (Capes, Brazil).

[EN] Background Alternans have been associated with the development of ventricular fibrillation and its control has been proposed as antiarrhythmic strategy. However, cardiac arrhythmias are a spatiotemporal phenomenon in which multiple factors are involved (e.g. calcium and voltage spatial alternans or heterogeneous conduction velocity) and how an antiarrhythmic drug modifies these factors is poorly understood. Objective The objective of the present study is to evaluate the relation between spatial electrophysiological properties (i.e. spatial discordant alternans and conduction velocity) and the induction of ventricular fibrillation (VF) when a calcium blocker is applied. Methods The mechanisms of initiation of VF were studied by simultaneous epicardial voltage and calcium optical mapping in isolated rabbit hearts using an incremental fast pacing protocol. The additional value of analyzing spatial phenomena in the generation of unidirectional blocks and reentries as precursors of VF was depicted. Specifically, the role of action potential duration (APD), calcium transients (CaT), spatial alternans and conduction velocity in the initiation of VF was evaluated during basal conditions and after the administration of verapamil. Results Our results enhance the relation between (1) calcium spatial alternans and (2) slow conduction velocities with the dynamic creation of unidirectional blocks that allowed the induction of VF. In fact, the administration of verapamil demonstrated that calcium and not voltage spatial alternans were the main responsible for VF induction. Conclusions VF induction at high activation rates was linked with the concurrence of a low conduction velocity and high magnitude of calcium alternans, but not necessarily related with increases of APD. Verapamil can postpone the development of cardiac alternans and the apparition of ventricular arrhythmias., This work was funded in part by the CIBERCV (Centro de Investigacion Biomedica en Red Enfermedades Cardiovasculares), Instituto de Salud Carlos III (PI14/00857, PI16/01123, DTS16/0160, PI17/01059, PI17/01106 and IJCI-2014-22178); Spanish Ministry of Ecomomy (TEC2013-46067-R); Generalitat Valenciana Grants (APOSTD/2017 and APOSTD/2018) and projects (GVA/2018/103), EIT-Health 19600 AFFINE and cofound by FEDER.

[EN] Background-Several metabolic conditions can cause the Brugada ECG pattern, also called Brugada phenotype (BrPh). We aimed to define the clinical characteristics and outcome of BrPh patients and elucidate the mechanisms underlying BrPh attributed to hyperkalemia. Methods and Results-We prospectively identified patients hospitalized with severe hyperkalemia and ECG diagnosis of BrPh and compared their clinical characteristics and outcome with patients with hyperkalemia but no BrPh ECG. Computer simulations investigated the roles of extracellular potassium increase, fibrosis at the right ventricular outflow tract, and epicardial/endocardial gradients in transient outward current. Over a 6-year period, 15 patients presented severe hyperkalemia with BrPh ECG that was transient and disappeared after normalization of their serum potassium. Most patients were admitted because of various severe medical conditions causing hyperkalemia. Six (40%) patients presented malignant arrhythmias and 6 died during admission. Multiple logistic regression analysis revealed that higher serum potassium levels (odds ratio, 15.8; 95% CI, 3.1-79; P=0.001) and male sex (odds ratio, 17; 95% CI, 1.05-286; P=0.045) were risk factors for developing BrPh ECG in patients with severe hyperkalemia. In simulations, hyperkalemia yielded BrPh by promoting delayed and heterogeneous right ventricular outflow tract activation attributed to elevation of resting potential, reduced availability of inward sodium channel conductance, and increased right ventricular outflow tract fibrosis. An elevated transient outward current gradient contributed to, but was not essential for, the BrPh phenotype. Conclusions-In patients with severe hyperkalemia, a BrPh ECG is associated with malignant arrhythmias and all-cause mortality secondary to resting potential depolarization, reduced sodium current availability, and fibrosis at the right ventricular outflow tract., This work was funded in part by the CIBERCV (Centro de Investigacion Biomedica en Red Enfermedades Cardiovasculares), Instituto de Salud Carlos III (PI14/00857, DTS16/0160, PI17/1059, PI01106), Spanish Ministry of Ecomomy (TEC2013-46067-R) and the ERDF (European Regional Development Fund).

[EN] Purpose: Atrial tachycardia (AT), flutter (AFL) and fibrillation (AF) are very common cardiac arrhythmias and are driven by localized sources that can be ablation targets. Non-invasive body surface potential mapping (BSPM) can be useful for early diagnosis and ablation planning. We aimed to characterize and differentiate the arrhythmic mechanisms behind AT, AFL and AF from the BSPM perspective using basic features reflecting their electrophysiology. Methods: 19 simulations of 567-lead BSPMs were used to obtain dominant frequency (DF) maps and estimate the atrial driving frequencies using the highest DF (HDF). Regions with vertical bar DF - HDF vertical bar, This study was supported in part by grants from Sao Paulo Research Foundation (2017/19775-3), Instituto de Salud Carlos III Research Foundation, Fondo Europeo de Desarrollo Regional FEDER, Spain (PI17/01106), and Generalitat Valenciana, Spain (AICO/2018/26,7 APOSTD/2017, GVA/2018/103).

INTRODUCTION Ablation of high dominant frequency (DF) sources in patients with atrial fibrillation (AF) is an effective treatment option for paroxysmal AF. The aim of this study was to evaluate the accuracy of noninvasive estimation of DF and electrical patterns determination by solving the inverse problem of the electrocardiography. METHODS Four representative AF patients with left-to-right and right-to-left atrial DF patterns were included in the study. For each patient, intracardiac electrograms from both atria were recorded simultaneously together with 67-lead body surface recordings. In addition to clinical recordings, realistic mathematical models of atria and torso anatomy with different DF patterns of AF were used. For both mathematical models and clinical recordings, inverse-computed electrograms were compared to intracardiac electrograms in terms of voltage, phase, and frequency spectrum relative errors. RESULTS Comparison between intracardiac and inverse computed electrograms for AF patients showed 8.8 ± 4.4% errors for DF, 32 ± 4% for voltage, and 65 ± 4% for phase determination. These results were corroborated by mathematical simulations showing that the inverse problem solution was able to reconstruct the frequency spectrum and the DF maps with relative errors of 5.5 ± 4.1%, whereas the reconstruction of the electrograms or the instantaneous phase presented larger relative errors (i.e., 38 ± 15% and 48 ± 14 % respectively, P < 0.01). CONCLUSIONS Noninvasive reconstruction of atrial frequency maps can be achieved by solving the inverse problem of electrocardiography with a higher accuracy than temporal distribution patterns.

[EN] Rotor-guided ablation has opened new perspectives into the therapy of atrial fibrillation (AF). Analysis of the spatio-temporal cardiac excitation patterns in the frequency and phase domains has demonstrated the importance of rotors in research models of AF, however, the dynamics and role of rotors in human AF are still controversial. In this review, the current knowledge gained through research models and patient data that support the notion that rotors are key players in AF maintenance is summarized. We report and discuss discrepancies regarding rotor prevalence and stability in various studies, which can be attributed in part to methodological differences among mapping systems. Future research for validation and improvement of current clinical electrophysiology mapping technologies will be crucial for developing mechanistic-based selection and application of the best therapeutic strategy for individual AF patient, being it, pharmaceutical, ablative, or other approach., This work was supported in part by grants from the Instituto de Salud Carlos III (Ministry of Economy and Competitiveness, Spain: PI13-01882, PI13-00903, and PI14/00857), Spanish Society of Cardiology (Clinical Research Grant 2015), Generalitat Valenciana (ACIF/2013/021), Innovation (Red RIC, PLE2009-0152), and NHLBI (P01-HL039707, P01-HL087226, and R01-HL118304).

There has been a recent upsurge in the development of electrocardiographic imaging (ECGI) methods, along with a significant increase in clinical application. To better assess the state-of-the-art, enable reliable progress, and facilitate clinical adoption, it is important to be able to compare results in a comprehensive manner, scientifically and clinically. However, studies vary in modeling choices, computational methods, validation mechanisms and metrics, and clinical applications, making unified evaluation and comparison of ECGI a critical challenge. This paper describes initial results of a project to address this challenge via a community-based approach organized by the Consortium for Electrocardiographic Imaging (CEI). We detail different aspects of this collective effort including a data sharing repository, a platform for comparison of different algorithms and modeling approaches on the same datasets, several active workgroups and progress made along these directions. We also summarize the results from groups participating in this collaboration and contributing solutions by applying their methods to the same dataset for comparison.

[EN] Atrial Flutter (AFL) termination by ablating the path responsible for the arrhythmia maintenance is an extended practice. However, the difficulty associated with the identification of the circuit in the case of atypical AFL motivates the development of diagnostic techniques. We propose body surface phase map analysis as a non-invasive tool to identify AFL circuits. Sixty seven lead body surface recordings were acquired in 9 patients during AFL (i.e. 3 typical, 6 atypical). Computed body surface phase maps from simulations of 5 reentrant behaviors in a realistic atrial structure were also used. Surface representation of the macro-reentrant activity was analyzed by tracking the singularity points (SPs) in surface phase maps obtained from band-pass filtered body surface potential maps. Spatial distribution of SPs showed significant differences between typical and atypical AFL. Whereas for typical AFL patients 70.78 +/- 16.17% of the maps presented two SPs simultaneously in the areas defined around the midaxialliary lines, this condition was only satisfied in 5.15 +/- 10.99% (p < 0.05) maps corresponding to atypical AFL patients. Simulations confirmed these results. Surface phase maps highlights the reentrant mechanism maintaining the arrhythmia and appear as a promising tool for the noninvasive characterization of the circuit maintaining AFL. The potential of the technique as a diagnosis tool needs to be evaluated in larger populations and, if it is confirmed, may help in planning ablation procedures., Supported in part by: Instituto de Salud Carlos III FEDER (Fondo Europeo de Desarrollo Regional; IJCI- 2014-22178, DTS16/00160; PI16/01123; PI17/01059; PI17/01106), Spanish Ministry of Education and Generalitat Valenciana Grants APOSTD/2017 and APOSTD/2018. The authors would like to thank Victoria Donis, Maria Mozo and Natividad Mihi for their help in data collection.

[EN] Electrocardiographic imaging (ECGI) reconstructs the electrical activity of the heart from a dense array of body-surface electrocardiograms and a patient-specific heart-torso geometry. Depending on how it is formulated, ECGI allows the reconstruction of the activation and recovery sequence of the heart, the origin of premature beats or tachycardia, the anchors/hotspots of re-entrant arrhythmias and other electrophysiological quantities of interest. Importantly, these quantities are directly and non-invasively reconstructed in a digitized model of the patient's three-dimensional heart, which has led to clinical interest in ECGI's ability to personalize diagnosis and guide therapy. Despite considerable development over the last decades, validation of ECGI is challenging. Firstly, results depend considerably on implementation choices, which are necessary to deal with ECGI's ill-posed character. Secondly, it is challenging to obtain (invasive) ground truth data of high quality. In this review, we discuss the current status of ECGI validation as well as the major challenges remaining for complete adoption of ECGI in clinical practice. Specifically, showing clinical benefit is essential for the adoption of ECGI. Such benefit may lie in patient outcome improvement, workflow improvement, or cost reduction. Future studies should focus on these aspects to achieve broad adoption of ECGI, but only after the technical challenges have been solved for that specific application/pathology. We propose 'best' practices for technical validation and highlight collaborative efforts recently organized in this field. Continued interaction between engineers, basic scientists, and physicians remains essential to find a hybrid between technical achievements, pathological mechanisms insights, and clinical benefit, to evolve this powerful technique toward a useful role in clinical practice., This study received financial support from the Hein Wellens Fonds, the Cardiovascular Research and Training Institute (CVRTI), the Nora Eccles Treadwell Foundation, the National Institute of General Medical Sciences of the National Institutes of Health (P41GM103545), the National Institutes of Health (NIH HL080093), the French government as part of the Investments of the Future program managed by the National Research Agency (ANR-10-IAHU-04), from the VEGA Grant Agency in Slovakia (2/0071/16), from the Slovak Research and Development Agency (APVV-14-0875), the Fondo Europeo de Desarrollo Regional (FEDER), the Instituto de Salud Carlos III (PI17/01106) and from Conselleria d'Educacio, Investigacio, Cultura i Esport de la Generalitat Valenciana (AICO/2018/267) and NIH grant (HL125998) and National Science Foundation (ACI-1350374).

The best definitive treatment option for end-stage heart failure currently is transplantation, which is limited by donor availability and immunorejection. Generating an autologous bioartificial heart could overcome these limitations. Here, we have decellularized a human heart, preserving its 3-dimensional architecture and vascularity, and recellularized the decellularized extracellular matrix (dECM). We decellularized 39 human hearts with sodium-dodecyl-sulfate for 4–8 days. Cell removal and architectural integrity were determined anatomically, functionally, and histologically. To assess cytocompatibility, we cultured human cardiac-progenitor cells (hCPC), bone-marrow mesenchymal cells (hMSCs), human endothelial cells (HUVECs), and H9c1 and HL-1 cardiomyocytes in vitro on dECM ventricles up to 21 days. Cell survival, gene expression, organization and/or electrical coupling were analyzed and compared to conventional 2-dimensional cultures. Decellularization removed cells but preserved the 3-dimensional cardiac macro and microstructure and the native vascular network in a perfusable state. Cell survival was observed on dECM for 21 days. hCPCs and hMSCs expressed cardiocyte genes but did not adopt cardiocyte morphology or organization; HUVECs formed a lining of endocardium and vasculature; differentiated cardiomyocytes organized into nascent muscle bundles and displayed mature calcium dynamics and electrical coupling in recellularized dECM. In summary, decellularization of human hearts provides a biocompatible scaffold that retains 3-dimensional architecture and vascularity and that can be recellularized with parenchymal and vascular cells. dECM promotes cardiocyte gene expression in stem cells and organizes existing cardiomyocytes into nascent muscle showing electrical coupling. These findings represent a first step toward manufacturing human heart grafts or matrix components for treating cardiovascular disease.

Experimental and clinical data demonstrate that atrial fibrillation (AF) maintenance in animals and groups of patients depends on localized reentrant sources localized primarily to the pulmonary veins (PVs) and the left atrium(LA) posterior wall in paroxysmal AF but elsewhere, including the right atrium (RA), in persistent AF. Moreover, AF can be eliminated by directly ablating AF-driving sources or “rotors,” that exhibit high-frequency, periodic activity. The RADAR-AF randomized trial demonstrated that an ablation procedure based on a more target-specific strategy aimed at eliminating high frequency sites responsible for AF maintenance is as efficacious as and safer than empirically isolating all the PVs. In contrast to the standard ECG, global atrial noninvasive frequency analysis allows non-invasive identification of high-frequency sources before the arrival at the electrophysiology laboratory for ablation. Body surface potential map (BSPM) replicates the endocardial distribution of DFs with localization of the highest DF (HDF) and can identify small areas containing the high-frequency sources. Overall, BSPM had a sensitivity of 75% and specificity of 100% for capturing intracardiac EGMs as having LARA DF gradient. However, raw BSPM data analysis of AF patterns of activity showed incomplete and instable reentrant patterns of activation. Thus, we developed an analysis approach whereby a narrow band-pass filtering allowed selecting the electrical activity projected on the torso at the HDF, which stabilized the projection of rotors that potentially drive AF on the surface. Consequently, driving reentrant patterns (“rotors”) with spatiotemporal stability during >70% of the AF time could be observed noninvasibly after HDF-filtering. Moreover, computer simulations found that the combination of BSPM phase mapping with DF analysis enabled the discrimination of true rotational patterns even during the most complex AF. Altogether, these studies show that the combination of DF analysis with phase maps of HDF-filtered surface ECG recordings allows noninvasive localization of atrial reentries during AF and further a physiologically-based rationale for personalized diagnosis and treatment of patients with AF.

Mechanical stretch induces an increase in Na+ influx into myocytes, related to mechanisms including stretch-activated channels or activation of Na+/H+ exchanger, involving changes in electrophysiological properties favouring arrhythmia induction. The aim of this study is to investigate the effects of mechanical stretch in cultured atrial murine cells (HL-1 myocytes) and its pharmacological response to ranolazine. Confluent HL-1 myocytes with spontaneous fibrillatory activity was cultured in silicone membrane plates, and were stretched to 110% of resting length. The response to stretch under control conditions and under ranolazine effects was analyzed using a high-resolution optical mapping system. HL-1 cells stretching increased atrial fibrillation dominant frequency in control conditions. The administration of ranolazine reduced this stretch-induced effect, and slowed the arrhythmia in baseline conditions. Ranolazine attenuates the modifications of electrophysiological effects induced by myocardial stretch in HL-1 cells model of AF.

Background: Slow-conducting channels in post-infarction scar areas constitute a life-threatening substrate, since they may lead to reentries and further fibrillation. Inverse problem methods may help to non-invasively locate these channels. Methods: Cardiac activity during chronic infarction was simulated based on Bueno-Orovio's cardiac cell model on a mesh representing a realistic ventricular anatomy. Ventricular electrograms (EGMs) were computed and used to obtain the torso surface electrical activity. The inverse problem resolution with zero-order Tikhonov regularization was used to calculate the inverse EGMs on the ventricular epicardial surface. Activation-recovery intervals (ARIs), defined as the interval between times of minimum derivative of QRS and maximum derivative of T wave, were measured from the departing and inverse-computed EGMs and subsequently compared. Results: ARIs were shorter in the channel than in the border zone and healthy area, both in the simulated EGMs and in their inverse-computed counterparts. Conclusion: The results in this paper show that the channel inside a post infarction scar zone can be non-invasively identified by using inverse problem approaches.

Cardiac tissue conduction velocity (CV) is a relevant variable in the maintenance and stability of atrial fibrillation (AF). The main goal of this work is to investigate modulation of CV in human chronic AF by inter-subject variability in ionic channel properties, cellular environmental factors and tissue coupling, by using a population of mathematical models. A monodomain tissue model was developed on the basis of the Skibsbye human atrial cell model. In this model, nine parameters are varied leading to a total number of 500 tissue models (8×156 cells). Among these simulations, 126 models are found within the experimental physiological range of biomarkers obtained from 149 chronic AF patients. The resulting population of 126 tissue models covers the variability in the biomarkers observed in the experimental recordings. The specific balance between sodium current and diffusion coefficient modulates the CV, critical for arrhythmia inducibility. The developed population of tissue models establishes the basis for further research in ionic mechanisms that facilitate AF maintenance and in-silico evaluation of personalised drug treatments.

Regularization methods for the inverse problem face important challenges during atrial fibrillation (AF), so any a priori information available during the electrophysiological study may improve the solution. We propose a Tikhonov-based inverse problem formulation that incorporates extra information provided by noisy intracavitary measurements near the endocardium. Since we introduced two different sources of information (Tikhonov estimation and a priori measurements), two regularization parameters must be tuned using a generalized L-Curve method. Performance of this method is studied using several time-based metrics proposed in previous works. The method proposed improved estimations accuracy in sinus rhythm, simple and complex AF models.

The efficacy of antiarrhythmic drugs terminating atrial fibrillation (AF) is highly dependent on the specific genetic and epigenetic characteristics of each patient. In-silico simulations representing inter-subject variability may help in the identification of personalized ionic mechanisms underlying rotor dynamics. In this work, a population of 173 electrophysiological atrial tissue models is used to investigate the factors promoting variability in the response of AF dynamics to sodium (I Na ) and calcium (I CaL ) current block. During basal conditions 126 of the 173 (72.8%) models sustained stable reentries. Partial block of sodium terminated AF by collision between rotors in 64 models (50.8%) whereas partial calcium block produced AF termination in 38 (30.2%). Interestingly, 39 models which terminated by sodium block did not terminate by calcium block, whereas 13 which terminated by calcium block did not terminate with sodium block. Cardiac tissue simulations accounting for inter-subject variability predict that AF patients may require different treatments and will be an important tool in the classification of patients and personalized medicine.

Electrocardiographic Imaging (ECGI) has become an increasingly used technique for non-invasive diagnosis of cardiac arrhythmias, although the need for medical imaging technology to determine the anatomy hinders its introduction in the clinical practice. This work explores the ability of the L-curve curvature for identifying the location and orientation of the atrial surface inside the torso. Surface electrical signals from 31 mathematical models and four AF patients were used to estimate the optimal position of the atria inside the torso. The curvature of the L-curve from the Tikhonov method was measured after application of deviations in atrial position and orientation. Independent deviations in the atrial position were solved by finding the maximal L-curve curvature with an error of 1.7±2.4 mm in mathematical models and 9.1±11.5 mm in patients. Independent angular deviations were solved with an error of 5.8±7.1° in mathematical models and 12.4°±13.2° in patients. Under superimposed uncertainties in the 3 axis of translation and in the 3 axis of rotation, the error in location was of 2.3±3.2 mm and 6.4°±7.1° in mathematical models, and 7.9±10.7 mm and 10.0°±12.8° in patients. The curvature of L-curve is a useful marker for emending the inaccuracies the cardiac location and would allow to combine torso and heart anatomies extracted from different image techniques.

Electrocardiographic imaging (ECGI) has recently gained attention as a viable diagnostic tool for reconstructing cardiac electrical activity in normal hearts as well as in cardiac arrhythmias. However, progress has been limited by the lack of both standards and unbiased comparisons of approaches and techniques across the community, as well as the consequent difficulty of effective collaboration across research groups.. To address these limitations, we created the Consortium for Electrocardiographic Imaging (CEI), with the objective of facilitating collaboration across the research community in ECGI and creating standards for comparisons and reproducibility. Here we introduce CEI and describe its two main efforts, the creation of EDGAR, a public data repository, and the organization of three collaborative workgroups that address key components and applications in ECGI. Both EDGAR and the workgroups will facilitate the sharing of ideas, data and methods across the ECGI community and thus address the current lack of reproducibility, broad collaboration, and unbiased comparisons.

[EN] Background: Ablation of drivers maintaining atrial fibrillation (AF) has been demonstrated as an effective therapy. Drivers in the form of rapidly activated atrial regions can be noninvasively localized to either left or right atria (LA, RA) with body surface potential mapping (BSPM) systems. This study quantifies the accuracy of dominant frequency (DF) measurements from reduced-leads BSPM systems and assesses the minimal configuration required for ablation guidance. Methods: Nine uniformly distributed lead sets of eight to 66 electrodes were evaluated. BSPM signals were registered simultaneously with intracardiac electrocardiograms (EGMs) in 16 AF patients. DF activity was analyzed on the surface potentials for the nine leads configurations, and the noninvasive measures were compared with the EGM recordings. Results: Surface DF measurements presented similar values than panoramic invasive EGM recordings, showing the highest DF regions in corresponding locations. The noninvasive DFs measures had a high correlation with the invasive discrete recordings; they presented a deviation of 0.8 for leads configurations with 12 or more electrodes. Conclusions: Reduced-leads BSPM systems enable noninvasive discrimination between LA versus RA DFs with similar results as higher-resolution 66-leads system. Our findings demonstrate the possible incorporation of simplified BSPM systems into clinical planning procedures for AF ablation., This work was supported in part by Generalitat-Valenciana Grants [ACIF/2013/021]; Instituto de SaludCarlos III, Ministerio de Ciencia e Innovacion [PI13/00903, PI13-01882, PI14/00857, PI16/01123, TEC2013-46067-R, DTS16/0160 and IJCI-2014-22178] cofound by FEDER.; Spanish Society of Cardiology [Clinical research Grants 2015]; Ministerio de Ciencia e Innovacion [Red RICRD12.0042.0001]; and the National Heart, Lung, and Blood Institute [P01-HL039707, P01-HL087226 and R01-HL118304].

[EN] BACKGROUND Dominant frequency (DF) and rotor mapping have been proposed as noninvasive techniques to guide localization of drivers maintaining atrial fibrillation (AF). OBJECTIVE The purpose of this study was to evaluate the robustness of both techniques in identifying atrial drivers noninvasively under the effect of electrical noise or model uncertainties. METHODS Inverse-computed DFs and phase maps were obtained from 30 different mathematical AF simulations. Epicardial highest dominant frequency (HDF) regions and rotor location were compared with the same inverse-computed measurements after addition of noise to the ECG, size variations of the atria, and linear or angular deviations in the atrial location inside the thorax. RESULTS Inverse-computed electrograms (EGMs) individually correlated poorly with the original EGMs in the absence of induced uncertainties (0.45 +/- 0.12) and were worse with 10-dB noise (0.22 +/- 0.11), 3-cm displacement (0.01 +/- 0.02), or 36 degrees rotation (0.02 +/- 0.03). However, inverse-computed HDF regions showed robustness against induced uncertainties: from 82% +/- 18% match for the best conditions, down to 73% +/- 23% for 10-dB noise, 77% +/- 21% for 5-cm displacement, and 60% +/- 22% for 36 degrees rotation. The distance from the inverse-computed rotor to the original rotor was also affected by uncertainties: 0.8 +/- 1.61 cm for the best conditions, 2.4 +/- 3.6 cm for 10-dB noise, 4.3 +/- 3.2 cm for 4-cm displacement, and 4.0 +/- 2.1 cm for 36 degrees rotation. Restriction of rotor detections to the HDF area increased rotor detection accuracy from 4.5 +/- 4.5 cm to 3.2 +/- 3.1 cm (P < .05) with 0-dB noise. CONCLUSION The combination of frequency and phase-derived measurements increases the accuracy of noninvasive localization of atrial rotors driving AF in the presence of noise and uncertainties in atrial location or size., This work was supported in part by grants from Generalitat Valenciana (ACIF/2013/021); Instituto de Salud Carlos III-FEDER (Fondo Europeo de Desarrollo Regional) and Ministerio de Ciencia e Innovacion (PI13-01882, PI13-00903, PI14/00857, PI16/01123, IJCI-2014-22178, DTS16/00160 and Red RIC RD12.0042.0001); Spanish Society of Cardiology (Clinical Research Grant 2015); and the National Heart, Lung, and Blood Institute (P01-HL039707, P01-HL087226, and Q1 R01-HL118304). Dr. Atienza served on the advisory board of Medtronic and Sorin. Dr. Berenfeld received research support from Medtronic and St. Jude Medical; and is a cofounder and Scientific Officer of Rhythm Solutions, Inc., Research and Development Director for S.A.S. Volta Medical, and consultant to Acutus Medical. All other authors have reported that they have no relationships relevant to the contents of this paper to disclose.

Preclinical studies showed a relationship between high dominant frequency areas (HDFA) and wave fractionation, but evidence in patient who atrial fibrillation (AF) persists for long-term periods (persAF) it is not well defined. This study aims to assess the spatio-temporal organization characteristics at HDFAs is persAF and its impact after per standard pulmonary vein isolation (PVI). Eight persAF patients had a non-contact array catheter deployed into the left atrium to collect up to 2048 AF electrograms (AEG) for 15 s. AEGs were band-pass filtered (3–30 Hz) followed by ventricular far-field cancellation. DF between 4–10 Hz and its respective organization index (OI) were calculated (4 s with 50% overlap) to produce 3D DF and OI maps. HDFA defined as the regions within a 0.25 Hz drop from the highest DF were determined and their centre of gravity (CG) calculated. Highest DF sites showed a higher OI at their core when compared to the periphery (0.422±0.101 vs. 0.386±0.126, p=0.02) and increased again organization at sites distant from the HDFAs. Similarly, after PVI, OI remained higher as compared to their periphery (0.372±0.026 vs. 0.332±0.036, p=0.22), but with significant lower values when compared with baseline (p

Background: Ablation is an effective therapy in atrial fibrillation (AF) patients in which an electrical driver can be identified. Objective: The aim of this study is to present and discuss a novel and strictly non-invasive approach to map and identify atrial regions responsible for AF perpetuation. Methods: Surface potential recordings of 14 patients with AF were recorded using a 67-lead recording system. Singularity points (SPs) were identified in surface phase maps after band-pass filtering at the highest dominant frequency (HDF). Mathematical models of combined atria and torso were constructed and used to investigate the ability of surface phase maps to estimate rotor activity in the atrial wall. Results: The simulations show that surface SPs originate at atrial SPs, but not all atrial SPs are reflected at the surface. Stable SPs were found in AF signals during 8.3±5.7% vs. 73.1±16.8% of the time in unfiltered vs. HDF-filtered patient data respectively (p, This work was supported in part by the Spanish Society of Cardiology (Becas Investigacion Clinica 2009); the Universitat Politecnica de Valencia through its research initiative program; the Generalitat Valenciana grant (ACIF/2013/021); the Ministerio de Economia y Competitividad, Rod RIC; the Centro Nacional de Investigaciones Cardiovasculares (proyecto CNIC-13); the Coulter Foundation from the Biomedical Engineering Department, University of Michigan; the Gelman Award from the Cardiovascular Division, University of Michigan; the National Heart, Lung, and Blood Institute grants (P01411.039707, P01-1111187226, and R01-11L118304); and the Leducq Foundation. Dr Femandez-Aviles served on the advisory board of Medtronic and has received research funding from St Jude Medical Spain. Dr Berenfeld has received research support from Medtronic and St Jude Medical; he is a colbunder and scientific officer of Rhythm Solutions. None of the companies disclosed financed the research described in this article.

Anatomical and/or functional reentries have been proposed as one of the main mechanism of perpetuation of cardiac fibrillation processes. However, technical limitations have difficult the characterization of those reentries and are hampering the development of effective anti-arrhythmic treatments. The goal of this study is to present a novel technology to map with high resolution the center of fibrillation drivers in order to characterize the mechanisms of reentry. Cell cultures of human cardiac-like cells differentiated from pluripotent stem cells were analyzed with a novel microscopic optical mapping system. The pharmacological response to verapamil administration of each type of reentry was analyzed. In all analyzed cell cultures, a reentry was identified as the mechanism of maintenance of the arrhythmia. Interestingly, the administration of verapamil produced opposite effects on activation rate depending on the mechanisms of reentry (i.e. anatomical or functional). Microscopic optical mapping of reentries allows the identification of perpetuation mechanisms which has been demonstrated to be linked with different pharmacological response.

While a number of researchers have attempted to develop algorithms for automated classification of heart sounds over the last five decades, these studies have been inadequate in terms of clinical utility due to a number of important limitations. The PhysioNet/Computing in Cardiology 2016 Challenge seeks to facilitate the development of highly robust algorithms to perform automatic classification of heart sounds in a manner which overcomes the limitations of previous studies. The dataset consists of over 3000 phonocardiogram recordings, taken from several locations on the body, from both healthy and pathological adults and children. The classification task requires the algorithm to determine if a recording is normal, abnormal or cannot be scored (due to excessive noise/corruption of the signal). An implementation of a state-of-the-art segmentation algorithm has been provided by the Challenge organizers, leaving the primary focus of the Challenge on the classification task. For this task, we selected a number of features in both the time and frequency domains. For Phase I, our best overall score for the hidden test set was 0.78 (Sensitivity = 0.70, Specificity = 0.87). For Phase II, our best score was 0.7864 (Sensitivity = 0.733, Specificity = 0.8398).