80 research outputs found

    Remarks on Einstein solitons with certain types of potential vector field

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    We consider almost Einstein solitons (V,λ)(V,\lambda) in a Riemannian manifold when VV is a gradient, a solenoidal or a concircular vector field. We explicitly express the function λ\lambda by means of the gradient vector field VV and illustrate the result with suitable examples. Moreover, we deduce some geometric properties when the Ricci curvature tensor of the manifold satisfies certain symmetry conditions

    Machine learning approach and waves synchronization improvement for the localization of Atrial Flutter circuit based on the 12-leads ECG

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    International audienceThe localization of the Atrial flutter (AFL) is of great interest for ablation planification. Regardless the direction of rotation of the corresponding reentry loop, its left or right atrium origin needs to be known beforehand. This lo-calization is usually performed by using visual inspection of the 12-leads standard ECG that could be computerized. The aim of the study is to automatically classify the corresponding averaged F-waves by using one to five simple features. The averaged F-wave is computed by introducing a new multi-lead extension of a SVD based method for the wave resynchronization. A dataset of ECG recorded from 56 subjects and comprising 25 left AFL and 31 right AFL will train the clas-sifier. It is shown that the single lead SVD based wave synchronization is efficiently extended to 12 leads by computing the SVD of each group of waves for each lead and optimally combining the corresponding first singular values. From the subsequent averaged 12 leads F-wave, 3 groups (Gi) of features were extracted: G1-(min, max), G2-(integral of the negative, of the positive part), G3-(integral of the wave, integral of the absolute value of the wave). For each group 24 features are then computed to feed the learning algorithm. A wrapper approach using an exhaustive search for feature selection is applied to maximize the mean classification accuracy computed over one to five features for each group (Gi) applied to the 12 leads. The logistic regression (LR) model is used for the supervised classifications. The mean accuracy ranges for the three groups, without validations, are G1:[0.69-0.83], G2:[0.68-0.81], G3:[0.68-0.80] for one feature up to five. The maximum accuracy comes from G1 with five features and is equal to 93%. The corresponding selected features are [max(I), max(III), max(V3), min(aVL), min(V5)]. In order to check for the risk of model overfitting, a leave one out cross-validation (LOOCV) is performed with these five features and gives 86% for the accuracy. When using all the 24 features simultaneously, the corresponding accuracy without validation is 93% and 67% for the LOOCV

    Remarks on Riemann and Ricci solitons in (α,β)(\alpha,\beta)-contact metric manifolds

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    We study almost Riemann solitons and almost Ricci solitons in an (α,β)(\alpha,\beta)-contact metric manifold satisfying some Ricci symmetry conditions, treating the case when the potential vector field of the soliton is pointwise collinear with the structure vector field

    Improving Flutter Localization Performance by Optimizing the Inverse Dower Transform

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    International audienceA previous study showed the possibility to localize right or left flutter circuit origin using variability contained in vectorcardiographic loop parameters. The Inverse Dower Transform, used to obtain the vectorcardiograms is based on a very simplistic torso conductor model, and hence not optimized. The present study aims to optimize the transform to maximize classifier accuracy. A parametric optimization model was proposed, as well as an optimization scheme. Model parameters were obtained by iteratively optimizing the linear SVM classifier accuracy until convergence. The goal can be shown to be multimodal and non-smooth. Therefore, a multi-instance and derivative-free method was considered. Previous dataset of 56 flutter recordings (31 right, 25 left) was used, considering only non-overlapped and respiratory motion-corrected F loops. For the SVM classifier, a 3.8% increase in accuracy was observed (max 0.95). When the logistic regression clas-sifier was used, an increase of 7.8% was observed (max 0.98). Comparison to a targeted transform previously developed showed an improvement by 17−19%. Observation of the model parameter values showed amplitude reduction applied to Lead X and rotation applied to Lead Z

    Non-Invasive Localization of Atrial Flutter Circuit using Recurrence Quantification Analysis and Machine Learning

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    International audienceAtrial flutter presents quasi-periodic atrial activity due to circular depolarization. Given the different structure of right and left atria, spatiotemporal variability should be different. This was analyzed using recurrence quan-tification analysis. Autocorrelation signals were estimated from the unthresholded recurrence plot, calculated with a properly processed ECG to remove variability related to external sources (noise, respiratory motion, T wave overlap). Simple features were considered from the autocorre-lation that attempts to describe the atrial activity in terms of range of recurrence and periodicity. Linear classification using support vector machines and logistic regression both allowed good classification performance (max accuracy 0.8 for both). Feature selection showed that right and left AFL have significantly different cycle lengths (right vs. left: 230.63 ms vs. 206.50 ms, p < 0.01). 1. Introduction The quasi-periodic atrial activity (AA) observed on the electrocardiogram (ECG) during atrial flutter (AFL) is caused by a rotating circular depolarization of the atrium. It has been shown that beat-to-beat variability of the flutter or F waves, quantified using vectorcardiographic parameters , allowed localization of right or left atrial circuit [1]. Different variability was observed for right and left local-ization, inducing a hypothesis of varying circuit stability. With a beat-to-beat approach, instantaneous spatiotem-poral information is not preserved, which may contain information about AA. In addition, both atria are known to be remarkably different in structure. The right atrium contains many large and well-defined cardiac fibers and is relatively thin, whereas the left atrium is thick and multi-layered [2]. It is expected that spatiotemporal variability would be different. The use of recurrence quantification analysis (RQA) has been highlighted for spatiotemporal analysis and characterization of atrial fibrillation (AF) activation propagation [3, 4]. Of particular interest, atrial fibrillation recurrence behavior was characterized, and was shown to be different for recurring and non-recurring persistent AF. In this paper, RQA is employed in order to study the spatiotemporal variability related to the circular propagation of AFL activation in a non-invasive fashion. Several features are extracted from the computed recurrence signal and serves as features for classification of circuit localiza-tion. Machine learning techniques are considered in order to obtain practical classifiers as well as to understand the reason why right and left AFL are different by employing feature selection

    0166: New measurement of A/V ratio on the mitral annulus: interest in ablation

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    IntroductionMany ablations require radiofrequency delivery near to the mitral annulus (MA).No reliable data exists about the electrical criteria of mitral annulus localisation. The aim of this study was to measure the A/V ratio on the mitral annulus and compare it to the A/V ratio on its atrial and ventricular side with transesophageal echocardiographic guidance and catheter tissue contact monitoring.MethodsTen patients in sinus rhythm undergoing atrial fibrillation catheter ablation under general anesthesia using a contact-force sensing catheter were included. After double transseptal puncture, we recorded the atrial and ventricular potentials on the mitral annulus at four defined points (3,6,9 and 12 o’clock), with direct confirmation of the position of the catheter relative to the mitral annulus by transesophageal echocardio-graphy and contact assessment by the force sensor on the catheter tip. Then we performed the same procedure on the atrial and ventricular sides of the mitral annulus.ResultsThere is a homogeneous distribution of the amplitude of the atrial and ventricular electrograms on the mitral annulus with a good correlation (r=0,93; p < 0,0001). The mean A/V ratio was 0,57 (± 0,078, IC 95% 0,540,59) on the mitral annulus, 0,725 (± 0,09, IC 0,65-0,79) on the atrial side and 0,348 (± 0,09, IC 95% 0,18-0,41) on the ventricular side near to the mitral annulus. These results were significantly different (p< 0,0001). No correlation was found between this ratio and the size of the left atrium, left ventricular mass and the presence of hypertension.ConclusionsA/V ratio on the MA is 0.57. It is significantly different from the A/V ratio on the atrial and ventricular sides of the MA, and may be used as an electrical criterion for MA localisation during ablation proceduresAbstract 0166 – Figur

    Technological advances in cardiac pacing and defibrillation

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    Since more than a half century, cardiac pacing and defibrillation represent a field in constant evolution, and they have shown some great technological advances from its conception to its methods of insertion. In this review, the recent developments about the accesses for pacemakers and ICD will be described: the axillary and the femoral vein. The His bundle pacing and the advantages of the entirely subcutaneous defibrillator will also be presented

    Safety of pulsed field ablation in more than 17,000 patients with atrial fibrillation in the MANIFEST-17K study

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    Pulsed field ablation (PFA) is an emerging technology for the treatment of atrial fibrillation (AF), for which pre-clinical and early-stage clinical data are suggestive of some degree of preferentiality to myocardial tissue ablation without damage to adjacent structures. Here in the MANIFEST-17K study we assessed the safety of PFA by studying the post-approval use of this treatment modality. Of the 116 centers performing post-approval PFA with a pentaspline catheter, data were received from 106 centers (91.4% participation) regarding 17,642 patients undergoing PFA (mean age 64, 34.7% female, 57.8% paroxysmal AF and 35.2% persistent AF). No esophageal complications, pulmonary vein stenosis or persistent phrenic palsy was reported (transient palsy was reported in 0.06% of patients; 11 of 17,642). Major complications, reported for ~1% of patients (173 of 17,642), were pericardial tamponade (0.36%; 63 of 17,642) and vascular events (0.30%; 53 of 17,642). Stroke was rare (0.12%; 22 of 17,642) and death was even rarer (0.03%; 5 of 17,642). Unexpected complications of PFA were coronary arterial spasm in 0.14% of patients (25 of 17,642) and hemolysis-related acute renal failure necessitating hemodialysis in 0.03% of patients (5 of 17,642). Taken together, these data indicate that PFA demonstrates a favorable safety profile by avoiding much of the collateral damage seen with conventional thermal ablation. PFA has the potential to be transformative for the management of patients with AF.Peer reviewe
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