Effect of Segmentation Uncertainty on the ECGI Inverse Problem Solution and Source Localization

International audienceElectrocardiographic Imaging (ECGI) is a promising tool to non-invasively map the electrical activity of the heart using body surface potentials (BSPs) and the patient specific anatomical data. One of the first steps of ECGI is the segmentation of the heart and torso geometries. In the clinical practice, the segmentation procedure is not fullyautomated yet and is in consequence operator-dependent. We expect that the inter-operator variation in cardiac segmentation would influence the ECGI solution. This effect remains however non quantified. In the present work, we study the effect of segmentation variability on the ECGI estimation of the cardiac activity with 262 shape models generated from fifteen different segmentations. Therefore, we designed two test cases: with and without shape model uncertainty. Moreover, we used four cases for ectopic ventricular excitation and compared the ECGI results in terms of reconstructed activation times and excitation origins. The preliminary results indicate that a small variation of the activation maps can be observed with a model uncertainty but no significant effect on the source localization is observed

Prediction of Clinical Deep Brain Stimulation Target for Essential Tremor From 1.5 Tesla MRI Anatomical Landmarks

International audienceBackground: Deep brain stimulation is an efficacious treatment for refractory essential tremor, though targeting the intra-thalamic nuclei remains challenging. Objectives: We sought to develop an inverse approach to retrieve the position of the leads in a cohort of patients operated on with optimal clinical outcomes from anatomical landmarks identifiable by 1.5 Tesla magnetic resonance imaging. Methods: The learning database included clinical outcomes and post-operative imaging from which the coordinates of the active contacts and those of anatomical landmarks were extracted. We used machine learning regression methods to build three different prediction models. External validation was performed according to a leave-one-out cross-validation. Results: Fifteen patients (29 leads) were included, with a median tremor improvement of 72% on the Fahn-Tolosa-Marin scale. Kernel ridge regression, deep neural networks, and support vector regression (SVR) were used. SVR gave the best results with a mean error of 1.33 ± 1.64 mm between the predicted target and the active contact position. Conclusion: We report an original method for the targeting in deep brain stimulation for essential tremor based on patients' radio-anatomical features. This approach will be tested in a prospective clinical trial

Numerical Investigation of Methods Used In Commercial Clinical Devices for Solving the ECGI Inverse Problem

Electrocardiographic Imaging (ECGI) is a promising tool to non-invasively map the electrical activity of the heart using body surface potentials (BSPs) combined with the patient specific anatomical data. In this work, we assess two ECGI algorithms used in commercial ECGI systems to solve the inverse problem; the Method of Fundamental Solutions (MFS) and the Equivalent Single Layer (ESL). We quantify the performance of these two methods in conjunction with two different activation maps to estimate the activation times and earliest activation sites. ESL provided more accurate reconstruction of the cardiac electrical activity, especially on the endocardial part of the heart. Nevertheless, both methods provided comparable results in terms of the derived activation maps and the localization of the focal origin as a clinically relevant parameter

Spiral Waves Generation Using an Eikonal-Reaction Cardiac Electrophysiology Model

International audienceAim: Computer models enabled the study of the fundamental mechanisms responsible for arrhythmias and have the potential of optimizing the clinical procedure for an individual patients pathology. The model complexity and the computational costs affecting computer models hamper their application on a routinely performed procedure. In this work, we aim to design a computer model suitable for clinical time scales.Methods: We adopt a (multi-front) eikonal model that adapts the conduction velocity to the underlying electrophysiology; we describe the diffusion current using a parametrised form, fitted to reproduce the monodomain profile. Results: We simulated spiral waves on a 3D tissue slab and bi-atrial anatomy. We compared the numerical results obtained with a monodomain formulation with those obtained with the new method. Both models provided the same pattern of the spiral waves. While the monodomain model presented slower propagation fronts, the eikonal model captured the correct value of the conduction velocity CV even using a coarse resolution. Conclusion: The eikonal model has the potential of enabling computer-guided procedures when adapts the conduction velocity to the underlying electrophysiology and characterises the diffusion current with a parametrised form

Inter-operator segmentation variability induces high premature ventricular contractions localization uncertainty at the heart base

International audienceBackgroundElectrocardiographic imaging (ECGI) is a promising tool for the treatment and diagnosis of cardiac arrhythmias. ECGI estimates non-invasively the electrical activity of the heart using body surface potentials (BSPs) obtained at the body surface in combination with a specific CT/MRI based anatomical models and defined electrode positions. In order to solve the ECGI inverse problem the first step to be considered is indeed the image segmentation and mesh generation.ObjectiveOur main purpose is to evaluate the effect of the inter-operator segmentation variability on the PVC localization.MethodsEight different cardiac segmentations from the same single subject CT-scans were performed by researchers within the consortium for Electrocardiographic Imaging. For all generated meshes, eight ventricular stimulation protocols were used; left and right ventricular free walls (LV, RV), apex, left and right ventricular outflow tract (LVOT, RVOT), septum, and two locations at the left and right heart base (LVB, RVB). BSPs were generated using computational models. We designed two test cases: with and without segmentation uncertainty. In test A, no segmentation uncertainty is considered. In test B, we solve the inverse problem for the eight geometries starting from one single BSP generated with a reference heart geometry. For each test case and for each stimulation protocol we computed the inverse solution using the Method of Fundamental Solutions and assessed the Localization Error (LE) of the pacing sites. In order to quantify the effect of segmentation uncertainty we also computed the difference between LEs obtained in tests B and A.ResultsIn test A, the mean LEs for LV, RV, apex, LVOT, RVOT, septum, LVB and RVB pacings are 7, 7, 5, 12, 14, 18, 13, 15 mm, respectively. In test B, the mean LEs are 7, 7, 5, 17, 23, 17, 16, 23 mm, respectively. The average differences between LEs are 0, 0, -1,5, 8, -1, 3, 8 mm, respectively.ConclusionThis study shows that the effect of the segmentation uncertainty on the localization of PVC is more important for RVOT, LVOT, RVB and LVB. We believe that the high uncertainty is due to the variability of segmentations at the base of the heart. These findings suggest that uncertainty in cardiac segmentation can have a significant impact on ECGI and its interpretability in clinical applications; therefore, careful segmentation is strongly recommended, especially at the base of the heart

Benchmark of deep learning algorithms for the automatic screening in electrocardiograms transmitted by implantable cardiac devices

International audienceThe objective of this work was to benchmark different deep learning architectures for noise detection against cardiac arrhythmia episodes recorded by pacemakers and implantable cardioverter-defibrillators (PM/ICDs) and transmitted for remote monitoring. Up to now, most signal processing from ICD data has been based on classical hand-crafted algorithms, not AI or DL-based ones. The database consist of PM/ICD data from 805 patients representing a total of 10471 recordings from three different channels: the right ventricular (RV), the right atria (RA), and the shock channel. Four deep learning approaches were trained and optimized to classify PM/ICDs' records as actual ventricular signal vs noise episodes. We evaluated the performance of the different models using the F 2 score. Results show that the use of 2D representations of 1D signals led to better performances than the direct use of 1D signals, suggesting that the detection of noise takes advantage of a spectral decomposition of the signal, which remains to be confirmed in other contexts. This study proposes deep learning approaches for the analysis of remote monitoring recordings from PM/ICDs. The detection of noise allows efficient management of this large daily flow of data

Artificial Intelligence for Detection of Ventricular Oversensing Machine Learning Approaches for Noise Detection Within Non-Sustained Ventricular Tachycardia Episodes Remotely Transmitted by Pacemakers and Implantable Cardioverter Defibrillators

International audienceBackground:Pacemakers (PMs) and implantable cardioverter defibrillators (ICDs) increasingly automatically record and remotely transmit non-sustained ventricular tachycardia (NSVT) episodes which may reveal ventricular oversensing.Objectives:We aimed to develop and validate a machine learning algorithm which accurately classifies NSVT episodes transmitted by PMs and ICDs in order to lighten healthcare workload burden and improve patient safety.Methods:PMs or ICDs (Boston Scientific) from four French hospitals with ≥1 transmitted NSVT episode were split into three subgroups: training set, validation set, and test set. Each NSVT episode was labelled as either physiological or non-physiological. Four machine learning algorithms (2DTF-CNN, 2D-DenseNet, 2DTF-VGG, and 1D-AgResNet) were developed using a training and validation dataset. Accuracies of the classifiers were compared with an analysis of the remote monitoring team of the Bordeaux University Hospital using F2 scores (favoring sensitivity over predictive positive value) using an independent test set.Results:807 devices transmitted 10.471 NSVT recordings (82% ICD, 18% PM), of which 87 devices (10.8%) transmitted 544 NSVT recordings with non-physiological signals. The classification by the remote monitoring team resulted in an F2 score of 0,932 (sensitivity of 95%, specificity of 99%) The four machine learning algorithms showed high and comparable F2 scores (2DTF-CNN: 0,914, 2D-DenseNet: 0,906, 2DTF-VGG: 0,863, 1D-AgResNet: 0,791) and only 1D-AgResNet had significantly different labeling as compared with the remote monitoring team.Conclusion:Machine learning algorithms were accurate in detecting non-physiological signals within EGMs transmitted by pacemaker and ICDs. An artificial intelligence approach may render remote monitoring less resourceful and improve patient safety