10 results on '"Néjib Zemzemi"'
Search Results
2. Prediction of Clinical Deep Brain Stimulation Target for Essential Tremor From 1.5 Tesla MRI Anatomical Landmarks
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Julien Engelhardt, Emmanuel Cuny, Dominique Guehl, Pierre Burbaud, Nathalie Damon-Perrière, Camille Dallies-Labourdette, Juliette Thomas, Olivier Branchard, Louise-Amélie Schmitt, Narimane Gassa, and Nejib Zemzemi
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essential tremor ,Vim nucleus ,deep brain stimulation (DBS) surgery ,brain surgery ,neurosurgery ,machine learning ,Neurology. Diseases of the nervous system ,RC346-429 - Abstract
Background: 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.
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- 2021
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3. Cardiac Activation Maps Reconstruction: A Comparative Study Between Data-Driven and Physics-Based Methods
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Amel Karoui, Mostafa Bendahmane, and Nejib Zemzemi
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data-driven approaches ,physics-based approaches ,ECGI inverse problem ,cardiac activation mapping ,neural networks ,deep learning ,Physiology ,QP1-981 - Abstract
One of the essential diagnostic tools of cardiac arrhythmia is activation mapping. Noninvasive current mapping procedures include electrocardiographic imaging. It allows reconstructing heart surface potentials from measured body surface potentials. Then, activation maps are generated using the heart surface potentials. Recently, a study suggests to deploy artificial neural networks to estimate activation maps directly from body surface potential measurements. Here we carry out a comparative study between the data-driven approach DirectMap and noninvasive classic technique based on reconstructed heart surface potentials using both Finite element method combined with L1-norm regularization (FEM-L1) and the spatial adaptation of Time-delay neural networks (SATDNN-AT). In this work, we assess the performance of the three approaches using a synthetic single paced-rhythm dataset generated on the atria surface. The results show that data-driven approach DirectMap quantitatively outperforms the two other methods. In fact, we observe an absolute activation time error and a correlation coefficient, respectively, equal to 7.20 ms, 93.2% using DirectMap, 14.60 ms, 76.2% using FEM-L1 and 13.58 ms, 79.6% using SATDNN-AT. In addition, results show that data-driven approaches (DirectMap and SATDNN-AT) are strongly robust against additive gaussian noise compared to FEM-L1.
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- 2021
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4. Clinical VIM targeting for deep brain stimulation based on an augmented intelligence. The RebrAIn solution
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Julien Engelhardt, Nejib Zemzemi, Dominique Guehl, Pierre Burbaud, Nathalie Damon-Perriere, and Emmanuel Cuny
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Neurosciences. Biological psychiatry. Neuropsychiatry ,RC321-571 - Published
- 2021
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5. MUSIC: Cardiac Imaging, Modelling and Visualisation Software for Diagnosis and Therapy
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Mathilde Merle, Florent Collot, Julien Castelneau, Pauline Migerditichan, Mehdi Juhoor, Buntheng Ly, Valery Ozenne, Bruno Quesson, Nejib Zemzemi, Yves Coudière, Pierre Jaïs, Hubert Cochet, and Maxime Sermesant
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cardiac imaging ,multimodal ,electrophysiology ,deep learning ,biophysical modelling ,inverse problems ,Technology ,Engineering (General). Civil engineering (General) ,TA1-2040 ,Biology (General) ,QH301-705.5 ,Physics ,QC1-999 ,Chemistry ,QD1-999 - Abstract
The tremendous advancement of cardiac imaging methods, the substantial progress in predictive modelling, along with the amount of new investigative multimodalities, challenge the current technologies in the cardiology field. Innovative, robust and multimodal tools need to be created in order to fuse imaging data (e.g., MR, CT) with mapped electrical activity and to integrate those into 3D biophysical models. In the past years, several cross-platform toolkits have been developed to provide image analysis tools to help build such software. The aim of this study is to introduce a novel multimodality software platform dedicated to cardiovascular diagnosis and therapy guidance: MUSIC. This platform was created to improve the image-guided cardiovascular interventional procedures and is a robust platform for AI/Deep Learning, image analysis and modelling in a newly created consortium with international hospitals. It also helps our researchers develop new techniques and have a better understanding of the cardiac tissue properties and physiological signals. Thus, this extraction of quantitative information from medical data leads to more repeatable and reliable medical diagnoses.
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- 2022
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6. An Analytical Model for the Effects of the Spatial Resolution of Electrode Systems on the Spectrum of Cardiac Signals
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Ferney A. Beltran-Molina, Jesus Requena-Carrion, Felipe Alonso-Atienza, and Nejib Zemzemi
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Cardiac arrhythmias ,dominant frequency ,bioelectric model ,spatiotemporal dynamics ,spatial resolution ,Electrical engineering. Electronics. Nuclear engineering ,TK1-9971 - Abstract
It has been suggested that the spatiotemporal characteristics of complex cardiac arrhythmias can be extracted from the spectrum of cardiac signals. However, the analysis of simple bioelectric models indicates that the spectrum of cardiac signals can be affected by the spatial resolution of the electrode system. In this paper, we derive exact measurement transfer functions relating the spectrum of cardiac signals to the spatiotemporal dynamics of cardiac sources. The analysis of the measurement transfer bandwidths for dynamics with different degrees of spatiotemporal correlation shows that as the spatial resolution decreases, the bandwidth of the measurement transfer function decreases until it reaches a constant value. Moreover, this transition from decreasing to constant values is determined by the degree of spatiotemporal correlation of the underlying cardiac source. Motivated by our analytical results, we investigate in a realistic computer simulation environment the impact of additive noise on the accuracy of body-surface dominant frequency (DF) maps. Our simulation results show that meaningful DF values are obtained on those locations where the analytical measurement transfer bandwidth is wide. These findings suggest that the accuracy of body-surface DF maps can be limited by the low spatial resolution of body-surface electrode systems.
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- 2017
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7. Evaluation of Fifteen Algorithms for the Resolution of the Electrocardiography Imaging Inverse Problem Using ex-vivo and in-silico Data
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Amel Karoui, Laura Bear, Pauline Migerditichan, and Nejib Zemzemi
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inverse problem ,Tikhonov regularization ,L1-norm regularization ,regularization parameter ,method of fundamental solutions ,finite element method ,Physiology ,QP1-981 - Abstract
The electrocardiographic imaging inverse problem is ill-posed. Regularization has to be applied to stabilize the problem and solve for a realistic solution. Here, we assess different regularization methods for solving the inverse problem. In this study, we assess (i) zero order Tikhonov regularization (ZOT) in conjunction with the Method of Fundamental Solutions (MFS), (ii) ZOT regularization using the Finite Element Method (FEM), and (iii) the L1-Norm regularization of the current density on the heart surface combined with FEM. Moreover, we apply different approaches for computing the optimal regularization parameter, all based on the Generalized Singular Value Decomposition (GSVD). These methods include Generalized Cross Validation (GCV), Robust Generalized Cross Validation (RGCV), ADPC, U-Curve and Composite REsidual and Smoothing Operator (CRESO) methods. Both simulated and experimental data are used for this evaluation. Results show that the RGCV approach provides the best results to determine the optimal regularization parameter using both the FEM-ZOT and the FEM-L1-Norm. However for the MFS-ZOT, the GCV outperformed all the other regularization parameter choice methods in terms of relative error and correlation coefficient. Regarding the epicardial potential reconstruction, FEM-L1-Norm clearly outperforms the other methods using the simulated data but, using the experimental data, FEM based methods perform as well as MFS. Finally, the use of FEM-L1-Norm combined with RGCV provides robust results in the pacing site localization.
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- 2018
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8. Chaste: an open source C++ library for computational physiology and biology.
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Gary R Mirams, Christopher J Arthurs, Miguel O Bernabeu, Rafel Bordas, Jonathan Cooper, Alberto Corrias, Yohan Davit, Sara-Jane Dunn, Alexander G Fletcher, Daniel G Harvey, Megan E Marsh, James M Osborne, Pras Pathmanathan, Joe Pitt-Francis, James Southern, Nejib Zemzemi, and David J Gavaghan
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Biology (General) ,QH301-705.5 - Abstract
Chaste - Cancer, Heart And Soft Tissue Environment - is an open source C++ library for the computational simulation of mathematical models developed for physiology and biology. Code development has been driven by two initial applications: cardiac electrophysiology and cancer development. A large number of cardiac electrophysiology studies have been enabled and performed, including high-performance computational investigations of defibrillation on realistic human cardiac geometries. New models for the initiation and growth of tumours have been developed. In particular, cell-based simulations have provided novel insight into the role of stem cells in the colorectal crypt. Chaste is constantly evolving and is now being applied to a far wider range of problems. The code provides modules for handling common scientific computing components, such as meshes and solvers for ordinary and partial differential equations (ODEs/PDEs). Re-use of these components avoids the need for researchers to 're-invent the wheel' with each new project, accelerating the rate of progress in new applications. Chaste is developed using industrially-derived techniques, in particular test-driven development, to ensure code quality, re-use and reliability. In this article we provide examples that illustrate the types of problems Chaste can be used to solve, which can be run on a desktop computer. We highlight some scientific studies that have used or are using Chaste, and the insights they have provided. The source code, both for specific releases and the development version, is available to download under an open source Berkeley Software Distribution (BSD) licence at http://www.cs.ox.ac.uk/chaste, together with details of a mailing list and links to documentation and tutorials.
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- 2013
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9. Méthodes numériques pour la résolution de problèmes inverses en électrocardiographie
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Karoui, Amel, Institut de Mathématiques de Bordeaux (IMB), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS), Université de Bordeaux, Néjib Zemzemi, Mostafa Bendahmane, STAR, ABES, Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS), Modélisation et calculs pour l'électrophysiologie cardiaque (CARMEN), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Inria Bordeaux - Sud-Ouest, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-IHU-LIRYC, and Université Bordeaux Segalen - Bordeaux 2-CHU Bordeaux [Bordeaux]-CHU Bordeaux [Bordeaux]
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Problème inverse ,Artificial neural networks ,Cardiac activation ,Activation cardiaque ,Numerical simulation ,Apprentissage automatique ,[MATH.MATH-NA] Mathematics [math]/Numerical Analysis [math.NA] ,[INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation ,Electrocardiography ,Simulation numérique ,Inverse problem ,Machine learning ,Réseaux de neurones ,[INFO.INFO-MO] Computer Science [cs]/Modeling and Simulation ,[MATH.MATH-NA]Mathematics [math]/Numerical Analysis [math.NA] ,Électrocardiographie - Abstract
In this thesis, we are interested in the mathematical modeling of cardiac electrophysiology and more precisely, the numerical study of the electrical activity of the heart. One of the challenges of this scientific theme is to reconstruct the electrical information on the cardiac surface from measurements taken on the thoracic surface. Such a problem is called the inverse problem. First, we analyze several methods of solving the inverse problem present in the literature and we propose a new approach of regularization based on the flow of electric current at the cardiac surface. The results are illustrated using simulated and experimental data. Next, we are interested in machine learning methods. Several models of artificial neural networks are created and developed to solve the inverse problem. We show that this approach improves the results of reconstruction of the cardiac electrical potential compared to classical inverse methods. Then, the greatest contribution of this thesis consists in the development of an artificial neural network model for cardiac activation mapping. The latter is characterized by a very great robustness against the noise previously present in the thoracic electrical signals. The last part is devoted to the comparison between the different models developed previously in order to determine the best numerical approach for cardiac activation mapping. The study is conducted using a simulated data set. We prove that methods based on machine learning provide the best results., Dans cette thèse, nous nous intéressons à la modélisation mathématique de l’électrophysiologie cardiaque et plus précisément, l’étude numérique de l’activité électrique du coeur. L’un des défis de cette thématique scientifique consiste à reconstruire l’information électrique à la surface cardiaque à partir des mesures réalisées à la surface thoracique. Un tel problème est appelé problème inverse. Dans un premier temps, nous analysons plusieurs méthodes de résolution du problème inverse présentes dans la littérature et nous proposons une nouvelle approche de régularisation basée sur le flux du courant électrique à la surface cardiaque. Les résultats sont illustrés moyennant des données simulées et expérimentales. Ensuite, nous nous intéressons aux méthodes d’apprentissage automatique. Plusieurs modèles de réseaux de neurones artificiels sont créés et développés pour résoudre le problème inverse. Nous montrons que cette approche améliore les résultats de reconstruction du potentiel électrique cardiaque par rapport aux méthodes inverses classiques. Puis, Le plus grand apport de cette thèse consiste au développement d’un modèle de réseau neuronal artificiel de cartographie d’activation cardiaque. Ce dernier se caractérise par une très grande robustesse face au bruit préalablement présent dans les signaux électriques thoraciques. La dernière partie est consacrée à la comparaison entre les différents modèles développés auparavant afin de déterminer la meilleure approche numérique de cartographie de l’activation cardiaque. L’étude est menée en utilisant un jeu de données simulées. Nous prouvons que les méthodes basées sur l’apprentissage automatique fournissent les meilleurs résultats.
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- 2021
10. Méthodes numériques pour la résolution de problèmes inverses en électrocardiographie
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KAROUI, Amel, Zemzemi, Néjib, Bendahmane, Mostafa, Albera, Laurent, Ben Abda, Amel, Vignon-Clémentel, Irène, Sermesant, Maxime, Turpault, Rodolphe, Néjib Zemzemi, Mostafa Bendahmane, Rodolphe Turpault [Président], Laurent Albera [Rapporteur], Amel Ben Abda [Rapporteur], Irène Vignon-Clémentel, and Maxime Sermesant
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Problème inverse ,Simulation numérique ,Activation cardiaque ,Réseaux de neurones ,Apprentissage automatique ,Électrocardiographie
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