La stimulation électrique neurale, appliquée au système nerveux périphérique pour la restauration des fonctions motrices ou la neuromodulation, est une technologie en plein essor, en particulier la stimulation implantée avec des électrodes Cuff positionnées autour d’un nerf périphérique. Le principal frein au développement des systèmes de stimulation est la difficulté à obtenir la stimulation ou l’inhibition des fonctions cibles de manière précise et indépendante, c’est-à-dire, obtenir une sélectivité des fonctions. Les paramètres impliqués dans la sélectivité au sens large ne sont pas toujours intuitifs, et le nombre de degrés de libertés (choix de l’électrode, nombre de contacts, forme du pulse etc.) est important. Tester toutes ces hypothèses en expérimentation n’est pas faisable et inenvisageable dans le réglage des neuroprothèses en contexte clinique. La modélisation a priori nous permet d’établir des critères de choix, de déterminer les stratégies les plus efficaces et de les optimiser. Par ailleurs, un grand nombre d’études ont pu prévoir des stratégies de sélectivité inédites grâce à la modélisation, et validées a posteriori par l’expérimentation. Le schéma de calcul scientifique est composé de deux parties. On modélise, d’une part, la propagation du champ de potentiels électriques générés par les électrodes à l’intérieur d’un volume conducteur représentant le nerf (étude biophysique), et d’autre part l’interaction entre ce champ de potentiels et les neurones (réponse électrophysiologique). Notre première contribution propose une méthode originale de modélisation et d’optimisation de la sélectivité spatiale avec une électrode Cuff, sans connaissance a priori de la topographie de nerf. Partant de ce constat, nous déterminons de nouveaux critères, l’efficacité et la robustesse, complémentaires à la sélectivité, nous permettant de faire un choix entre des configurations multipolaires concurrentes. Ainsi, en fonction de la pondération de ces critères, nous avons développé un algorithme d’optimisation pour déterminer la configuration optimale en fonction de la zone choisie, du diamètre des fibres visées ainsi que de la durée de stimulation, pour un pulse type rectangulaire de référence. Des expérimentations sur modèle animal nous ont permis d’évaluer l’efficacité de la méthode et sa généricité. Ce travail est partie intégrante d’un projet plus vaste de stimulation du nerf vague (projet INTENSE), où l’une des applications concerne le traitement des troubles cardiaques. L’objectif est d’activer sélectivement une population spécifique de fibres nerveuses pour obtenir des effets plus ciblés conduisant à une thérapie améliorée, tout en diminuant les effets secondaires. La deuxième contribution consiste à combiner la sélectivité spatiale et la sélectivité au diamètre de fibre avec un modèle générique de nerf et une électrode Cuff à 12 contacts. L’utilisation d’une forme d’onde particulière (prépulse) combinée avec des configurations multipolaires permet d’activer des fibres d’un diamètre défini dans un espace ciblé. Les perspectives cliniques sont nombreuses, notamment sur la réduction de la fatigue liée à l’utilisation prolongée de la stimulation ou la diminution des effets secondaires. Dans le cadre du projet INTENSE, la seconde application liée à la stimulation du nerf vague vise le problème de l’obésité morbide. L’activation des axones cibles liés aux fonctions gastriques nécessite une quantité de charges conséquente. Plusieurs études suggèrent que les formes de pulse non rectangulaires peuvent activer les axones du système nerveux périphérique avec une quantité de charges réduite comparée à la forme de pulse rectangulaire de référence. Notre dernière contribution concerne l’étude expérimentale et de modélisation de ces formes d’ondes complexes. L’approche par modélisation, si elle est bien maîtrisée, apporte une analyse pertinente voire même indispensable au réglage clinique des neuroprothèses. / Neural electrical stimulation, applied to the peripheral nervous system for motor functions restoration or neuromodulation, is a thriving technology, especially implanted stimulation using Cuff electrodes positioned around a peripheral nerve. The main obstacle to the development of stimulation systems is the difficulty in obtaining the independent stimulation or inhibition of specific target functions (i.e. functional selectivity). The parameters involved in selectivity are not always intuitive and the number of degrees of freedom (choice of electrode, number of contacts, pulse shape etc.) is substantial. Thus, testing all these hypotheses in a clinical context is not conceivable. This choice of parameters can be guided using prior numerical simulations predicting the effect of electrical stimulation on the neural tissue. Numerous studies developed new strategies to achieve selectivity based on modeling results that have been validated a posteriori by experimental works. The computation scheme is composed of two parts : the modeling of the potential field generated by the electrodes inside a conductive medium representing the nerve on the one hand; and the determination of the interaction between this field of potentials and neurons on the other. Our first contribution is an original method of modeling and optimization of the spatial selectivity with a Cuff electrode, without prior knowledge of the nerve topography. Based on this observation, we determined new criteria, efficiency and robustness, complementary to selectivity, allowing us to choose between multipolar configurations. Thus, according to the weighting applied to these criteria, we developed an optimization algorithm to determine the optimal configuration as a function of the target zone, fiber diameter and the stimulation duration for a typical rectangular pulse. Experiments on animal model allowed us to evaluate the effectiveness and genericness of the method. This work was performed as part of a larger project on vagus nerve stimulation (INTENSE project) in which one of the applications focused on the treatment of cardiac disorders. The main objective was to selectively activate a specific population of nerve fibers to improve therapy and decrease side effects. In a second contribution, numerical simulations were used to investigate the combination of multipolar configurations and the prepulses technique, in order to obtain fiber recruitment in a spatially reverse order. The main objective was to achieve both spatial and fiber diameter selectivity. Expected clinical perspectives of this work are the reduction of fatigue related to a prolonged use of stimulation and the reduction of side effects. Within the framework of the INTENSE project, the second application investigated vagus nerve stimulation as a therapy for morbid obesity. Activation of target axons related to gastric functions requires a significant amount of charge injection. Several studies suggest that non-rectangular waveforms can activate axons of the peripheral nervous system with a reduced amount of charge compared to the reference rectangular pulse shape. Our last contribution focuses on the experimental study and the modeling of these complex waveforms. The modeling approach, if performed properly and while bearing in mind its limits, provides a relevant and even indispensable analysis tool for the clinical adjustment of neuroprostheses.

Electrical Impedance Tomography (EIT) is a non linear inverse problem in which the electrical conductivity or resistivity distribution across a closed domain of interest is reconstructed from the surface potentials measured at the domain boundary by injecting a constant sinusoidal current through an array of surface electrodes. Being a non-invasive, non-radiating, non-ionizing, portable and inexpensive methodology, EIT has been extensively studied in medical diagnosis, biomedical engineering, biotechnology, chemical engineering, industrial and process engineering, civil and material engineering, soil and rock science, electronic industry, defense field, nano-technology and many other fields of applied physics. The reconstructed image quality in EIT depends mainly on the boundary data quality and the performance of the reconstruction algorithm used. The boundary data accuracy depends on the design of the practical phantoms, current injection method and boundary data measurement process and precision. On the other hand, the reconstruction algorithm performance is highly influenced by the mathematical modeling of the system, performance of the forward solver and Jacobian computation, inverse solver and the regularization techniques. Hence, for improving the EIT system performance, it is essential to improve the design of practical phantom, instrumentation and image reconstruction algorithm. As the electrical impedance of biological materials is a function of tissue composition and the frequency of applied ac signal, the better assessment of impedance distribution of biological tissues needs multifrequency EIT imaging. In medical EIT, to obtain a better image quality for a complex organ or a body part, accurate domain modelling with a large 3D finite element mesh is preferred and hence, the computation speed becomes very expensive and time consuming. But, the high speed reconstruction with improved image quality at low cost is always preferred in medical EIT. In this direction, a complete multifrequency multifunction EIT (MfMf-EIT) system is developed and multifrequency impedance reconstruction is studied to improve the bioimpedance imaging. The MfMf-EIT system consists of an MfMf-EIT instrumentation (MfMf-EITI), high speed impedance image reconstruction algorithms (IIRA), a Personal Computer (PC) and a number of practical phantoms with EIT sensors or electrodes. MfMf-EIT system and high speed IIRA are studied tested and evaluated with the practical phantoms and the multifrequency impedance imaging is improved with better image quality as well as fast image reconstruction. The MfMf-EIT system is also applied to the human subjects and the impedance imaging is studied for human body imaging and the system is evaluated. MfMf-EIT instrumentation (MfMf-EITI) consists of a multifrequency multifunction constant current injector (MfMf-CCI), multifrequency multifunction data acquisition system (MfMf DAS), a programmable electrode switching module (P-ESM) and a modified signal conditioner blocks (M-SCB) or data processing unit (DPU). MfMf-CCI, MfMf-DAS, P-ESM and M-SCBs are interfaced with a LabVIEW based data acquisition program (LV-DAP) controlled by a LabVIEW based graphical user interface (LV-GUI). LV-GUI controls the current injection and data acquisition with a user friendly, fast, reliable, efficient measurement process. The data acquisition system performance is improved by the high resolution NIDAQ card providing high precision measurement and high signal to noise ratio (SNR). MfMf-EIT system is developed as a versatile data acquisition system with a lot of flexibilities in EIT parameter selection that allows studying the image reconstruction more effectively. MfMf-EIT instrumentation controls the multifrequency and multifunctioned EIT experimentation with a number of system variables such as signal frequency, current amplitude, current signal wave forms and current injection patterns. It also works with either grounded load CCI or floating load CCI and collects the boundary data either in grounded potential form or differential form. The MfMf-EITI is futher modified to a battery based MfMf-EIT (BbMfMf-EIT) system to obtain a better patient safety and also to improve the SNR of the boundary data. MfMf-EIT system is having a facility of injecting voltage signal to the objects under test for conducting the applied potential tomography (APT). All the electronic circuit blocks in MfMf-EIT instrumentation are tested, evaluated and calibrated. The frequency response, load response, Fast Fourier Transform (FFT) studies and DSO analysis are conducted for studying the electronic performance and the signal quality of all the circuit blocks. They are all evaluated with both the transformer based power supply (TBPS) and battery based power supply (BBPS). MfMf-DAS, P-ESM and LV-DAP are tested and evaluated with digital data testing module (DDTM) and practical phantoms. A MatLAB-based Virtual Phantom for 2D EIT (MatVP2DEIT) is developed to generate accurate 2D boundary data for assessing the 2D EIT inverse solvers and its image reconstruction accuracy. It is a MATLAB-based computer program which defines a phantom domain and its inhomogeneities to generate the boundary potential data by changing its geometric parameters. In MatVP2DEIT, the phantom diameter, domain discretization, inhomogeneity number, inhomogeneity geometry (shape, size and position), electrode geometry, applied current magnitude, current injection pattern, background medium conductivity, inhomogeneity conductivity all are set as the phantom variables and are chosen indipendently for simulating different phantom configurations. A constant current injection is simulated at the phantom boundary with different current injection protocols and boundary potential data are calculated. A number of boundary data sets are generated with different phantom configurations and the resistivity images are reconstructed using EIDORS (Electrical Impedance Tomography and Diffuse Optical Tomography Reconstruction Software). Resistivity images are evaluated with the resistivity parameters and contrast parameters estimated from the elemental resistivity profiles of the reconstructed impedance images. MfMf-EIT system is studied, tested, evaluated with a number of practical phantoms eveloped with non-biological and biological materials and the multifrequency impedance imaging is improved. A number of saline phantoms with single and multiple inhomogeneities are developed and the boundary data profiles are studied and the phantom geometry is modified. NaCl-insulator phantoms and the NaCl-vegetable phantoms with different inhomogeneity configurations are developed and the multifrequency EIT reconstruction is studied with different current patterns, different current amplitudes and different frequencies using EIDORS as well as the developed IIRAs developed in MATLAB to evaluate the phantoms and MfMf-EIT system. Real tissue phantoms are developed with different chicken tissue backgrounds and high resistive inhomogeneities and the resistivity image reconstruction is studied using MfMf-EIT system. Chicken tissue phantoms are developed with chicken muscle tissue (CMTP) paste or chicken tissue blocks (CMTB) as the background mediums and chicken fat tissue, chicken bone, air hole and nylon cylinders are used as the inhomogeneity to obtained different phantom configurations. Resistivity imaging of all the real tissue phantoms is reconstructed in EIDORS and developed IIRAs with different current patterns, different frequencies and the images are evaluated by the image parameters to assess the phantoms as well as the MfMf-EIT system. Gold electrode phantoms are developed with thin film based flexible gold electrode arrays for improved bioimpedance and biomedical imaging. The thin film based gold electrode arrays of high geometric precision are developed on flexible FR4 sheet using electro-deposition process and used as the EIT sensors. The NaCl phantoms and real tissue phantoms are developed with gold electrode arrays and studied with MfMf-EIT system and and the resiulsts are compared with identical stainless steel electrode phantoms. NaCl phantoms are developed with 0.9% NaCl solution with single and multiple insulator or vegetable tissues as inhomogeneity. Gold electrode real tissue phantoms are also developed with chicken muscle tissues and fat tissues or other high resistive objects. The EIT images are reconstructed for the gold electrode NaCl phantoms and the gold electrode real tissue phantoms with different phantom geometries, different inhomogeneity configurations and different current patterns and the results are compared with identical SS electrode phantoms. High speed IIRAs called High Speed Model Based Iterative Image Reconstruction (HSMoBIIR) algorithms are developed in MATLAB for impedance image reconstruction in Electrical Impedance Tomography (EIT) by implementing high speed Jacobian calculation techniques using “Broyden’s Method (BM)” and “Adjoint Broyden’s Method (ABM)”. Gauss Newton method based EIT inverse solvers repeatitively evaluate the Jacobian (J) which consumes a lot of computation time for reconstruction, whereas, the HSMoBIIR with Broyden’s Methods (BM)-based accelerated Jacobian Matrix Calculators (JMCs) provides the high speed schemes for Jacobian (J) computation which is integrated with conjugate gradient scheme (CGS) for fast impedance reconstruction. The Broyden’s method based HSMoBIIR (BM-HSMoBIIR) and Adjoint Broyden’s method based HSMoBIIR (ABM-HSMoBIIR) algorithm are developed for high speed improved impedance imaging using BM based JMC (BM-JMC) and ABM-based JMC (ABM-JMC) respectively. Broyden’s Method based HSMoBIIR algorithms make explicit use of secant and adjoint information that can be obtained from the forward solution of the EIT governing equation and hence both the BM-HSMoBIIR and ABM-HSMoBIIR algorithms reduce the computational time remarkably by approximating the system Jacobian (J) successively through low-rank updates. The impedance image reconstruction is studied with BM-HSMoBIIR and ABM-HSMoBIIR algorithms using the simulated and practical phantom data and results are compared with a Gauss-Newton method based MoBIIR (GNMoBIIR) algorithm. The GNMoBIIR algorithm is developed with a Finite Element Method (FEM) based flexible forward solver (FFS) and Gauss-Newton method based inverse solver (GNIS) working with a modified Newton-Raphson iterative technique (NRIT). FFS solves the forward problem (FP) to obtain the computer estimated boundary potential data (Vc) data and NRIT based GNIS solve the inverse problem (IP) and the conductivity update vector [Δσ] is calculated by conjugate gradient search by comparing Vc measured boundary potential data (Vm) and using the Jacobian (J) matrix computed by the adjoint method. The conductivity reconstruction is studied with GNMoBIIR, BM-HSMoBIIR and ABM-HSMoBIIR algorithms using simulated data a practical phantom data and the results are compared. The reconstruction time, projection error norm (EV) and the solution error norm (Eσ) produced in HSMoBIIR algorithms are calculated and compared with GNMoBIIR algorithm. Results show that both the BM-HSMoBIIR and ABM-HSMoBIIR algorithms successfully reconstructs the conductivity distribution of the domain under test with its proper inhomogeneity and background conductivities for simulation as well as experimental studies. Simulated and practical phantom studies demonstrate that both the BM-HSMoBIIR and ABM-HSMoBIIR algorithms accelerate the impedance reconstruction by more than five times. It is also observed that EV and Eσ are reduced in both the HSMoBIIR algorithms and hence the image quality is improved. Noise analysis and convergence studies show that both the BM-HSMoBIIR and ABM-HSMoBIIR algorithms works faster and better in noisy conditions compared to GNMoBIIR. In low noise conditions, BM-HSMoBIIR is faster than to ABM-HSMoBIIR algorithm. But, in higher noisy environment, the ABM-HSMoBIIR is found faster and better than BM-HSMoBIIR. Two novel regularization methods called Projection Error Propagation-based Regularization (PEPR) and Block Matrix based Multiple Regularization (BMMR) are proposed to improve the image quality in Electrical Impedance Tomography (EIT). PEPR method defines the regularization parameter as a function of the projection error contributed by the mismatch (difference) between the data obtained from the experimental measurements (Vm) and calculated data (Vc). The regularization parameter in the reconstruction algorithm gets modified automatically according to the noise level in measured data and ill-posedness of the Hessian matrix. The L-2 norm of the projection error is calculated using the voltage difference and it is used to find the regularization parameter in each iteration in the reconstruction algorithm. In BMMR method, the response matrix (JTJ) obtained from the Jacobian matrix (J) has been partitioned into several sub-block matrices and the highest eigenvalue of each sub-block matrices has been chosen as regularization parameter for the nodes contained by that sub-block. The BMMR method preserved the local physiological information through the multiple regularization process which is then integrated to the ill-posed inverse problem to make the regularization more effective and optimum for all over the domain. Impedance imaging with simulated data and the practical phantom data is studied with PEPR and BMMR techniques in GNMoBIIR and EIDORS and the reconstructed images are compared with the single step regularization (STR) and Modified Levenberg Regularization (LMR). The projection error and the solution error norms are estimated in the reconstructions processes with PEPR and the BMMR methods and the results are compared with the errors estimated in STR and modified LMR techniques. Reconstructed images obtained with PEPR and BMMR are also studied with image parameters and contrast parameters and the reconstruction performance with PEPR and BMMR are evaluated by comparing the results with STR and modified LMR. PEPR and BMMR techniques are successfully implemented in the GNMoBIIR and EIDORS algorithms to improve the impedance image reconstruction by regularizing the solution domain in EIT reconstruction process. As the multifrequency EIT is always preferred in biological object imaging for better assessments of the frequency dependent bioimpedance response, multifrequency impedance imaging is studied with MfMf-EIT system developed for biomedical applications. MfMf-EIT system is studied, tested and evaluated with practical phantoms suitably developed for multifrequency impedance imaging within a wide range of frequency. Different biological materials are studied with electrical impedance spectroscopy (EIS) and a number of practical biological phantoms suitable for multifrequency EIT imaging are developed. The MfMf-EIT system is studied, tested and evaluated at different frequency levels with different current patterns using a number of NaCl phantoms with single, multiple and hybrid vegetable tissue phantoms as well as with chicken tissue phantoms. BbMfMf-EIT system is also studied and evaluated with the multifrequency EIT imaging using the developed biological phantoms. The developed MfMf-EIT system is applied on human body for impedance imaging of human anatomy. Impedance imaging of human leg and thigh is studied to visualize the muscle and bone tissues using different current patterns and different relative electrode positions. Ag/AgCl electrodes are attached to the leg and thigh using ECG gel and the boundary data are collected with MfMf-EIT EIT system by injecting a 1 mA and 50 kHz sinusoidal constant current with neighbouring and opposite current injection patterns. Impedance images of the femur bone of the human thigh and the tibia and fibula bones of the human leg along with the muscle tissue backgrounds are reconstructed in EIDORS and GNMoBIIR algorithms. Reconstructed resistivity profiles of bone and muscles are compared with the resistivity data profiles reported in the published literature. Impedance imaging of leg and thigh is studied with MfMf-EIT system for different current patterns, relative electrode positions and the images are evaluated to assess the system reliability. Battery based MfMf-EIT system (BbMfMf-EIT) is also studied for human leg and thigh imaging and it is observed that MfMf-EIT system and BbMfMf-EIT system are suitable for impedance imaging of human body imaging though the BbMfMf-EIT system increases the patiet safety. Therefore, the developed MfMf-EIT and BbMfMf-EIT systems are found quite suitable to improve the bio-impedance imaging in medical, biomedical and clinical applications as well as to study the anatomical and physiological status of the human body to diagnose, detect and monitor the tumors, lesions and a number of diseases or anatomical abnormalities in human subjects.