A wavelet based method for electrical stimulation artifacts removal in electromyogram

International audienceA technique for artifact removal based on the continuous wavelet transform is presented. It uses common mother wavelets to find the temporal localization of stimulation artifacts on electromyogram signal recording during an electrical surface evoked contraction of a muscle. This method is applied with different kinds of stimulation pulse parameters including shape and duration changes. This method is used with standard stimulation pulse waveforms such as monophasic or biphasic ones. It can also be applied when the artifacts and M waves are in the same range of amplitude where threshold techniques are inefficient. Lastly, a method to determine which mother wavelet efficiently removed artifacts is proposed, results indicate the Haar wavelet performs best among fourteen tested wavelets

Contribution à la conception d'un électromyostimulateur intelligent

This project aims to develop a new tool for neuromuscular reeducation. Its function is to improve the quality and the duration of muscular strengthening training sessions and training of motor function for patients suffering from muscle deconditioning. A "smart" electromyostimulator using, at the same time, techniques of electrostimulation (EMS) and analysis of electromyography (EMG) allows the control in real time electrical stimulation parameters considering the physiological fatigue of the stimulated muscle. This control, performed on stimulation parameters depending on electrical response of muscles (M wave), allows the muscle stimulation taking into account the muscular reaction to the electrical stimulationCette thèse a pour but de mettre au point un nouvel outil de rééducation neuromusculaire. Elle a pour fonction, l'amélioration de la qualité et de la durée des séances de renforcement musculaire et de réentraînement de la motricité de sujets atteints de déconditionnement musculaire. Un électromyostimulateur "intelligent" utilisant en même temps des techniques d'électromyostimulation (EMS) couplées aux analyses de l'électromyogramme (EMG) est développé et permet d'asservir en temps réel les paramètres de stimulation d'un muscle en fonction de son état de fatigue physiologique. Le contrôle ainsi effectué sur les paramètres de stimulation en fonction de la réponse musculaire électrique (onde M) offre la possibilité de stimuler un muscle en prenant en compte une information sur la réaction du muscle à l'électrostimulatio

Automatic detection of P, QRS and T patterns in 12 leads ECG signal based on CWT

International audienceIn this paper, a new method based on the continuous wavelet transform is described in order to detect the QRS, P and T waves. QRS, P and T waves may be distinguished from noise, baseline drift or irregular heartbeats. The algorithm, described in this paper, has been evaluated using the Computers in Cardiology (CinC) Challenge 2011 database and also applied on the MIT-BIH Arrhythmia database (MITDB). The data from the CinC Challenge 2011 are standard 12 ECG leads recordings with full diagnostic bandwidth compared to the MITDB which only includes two leads for each ECG signal. Firstly, our algorithm is validated using fifty 12 leads ECG samples from the CinC collection. The samples have been chosen in the "acceptable records" list given by Physionet. The detection and the duration delineation of the QRS, P and T waves given by our method are compared to expert physician results. The algorithm shows a sensitivity equal to 0.9987 for the QRS complex, 0.9917 for the T wave and 0.9906 for the P wave. The accuracy and the Youden index values show that the method is reliable for the QRS, T and P waves detection and delineation. Secondly, our algorithm is applied to the MITDB in order to compare the detection of QRS wave to results of other some works in the literature

Contribution to the design of a smart electromyostimulator

Cette thèse a pour but de mettre au point un nouvel outil de rééducation neuromusculaire. Elle a pour fonction, l'amélioration de la qualité et de la durée des séances de renforcement musculaire et de réentraînement de la motricité de sujets atteints de déconditionnement musculaire. Un électromyostimulateur "intelligent" utilisant en même temps des techniques d'électromyostimulation (EMS) couplées aux analyses de l'électromyogramme (EMG) est développé et permet d'asservir en temps réel les paramètres de stimulation d'un muscle en fonction de son état de fatigue physiologique. Le contrôle ainsi effectué sur les paramètres de stimulation en fonction de la réponse musculaire électrique (onde M) offre la possibilité de stimuler un muscle en prenant en compte une information sur la réaction du muscle à l'électrostimulationThis project aims to develop a new tool for neuromuscular reeducation. Its function is to improve the quality and the duration of muscular strengthening training sessions and training of motor function for patients suffering from muscle deconditioning. A "smart" electromyostimulator using, at the same time, techniques of electrostimulation (EMS) and analysis of electromyography (EMG) allows the control in real time electrical stimulation parameters considering the physiological fatigue of the stimulated muscle. This control, performed on stimulation parameters depending on electrical response of muscles (M wave), allows the muscle stimulation taking into account the muscular reaction to the electrical stimulatio

Contribution to the design of a smart electromyostimulator

Cette thèse a pour but de mettre au point un nouvel outil de rééducation neuromusculaire. Elle a pour fonction, l'amélioration de la qualité et de la durée des séances de renforcement musculaire et de réentraînement de la motricité de sujets atteints de déconditionnement musculaire. Un électromyostimulateur "intelligent" utilisant en même temps des techniques d'électromyostimulation (EMS) couplées aux analyses de l'électromyogramme (EMG) est développé et permet d'asservir en temps réel les paramètres de stimulation d'un muscle en fonction de son état de fatigue physiologique. Le contrôle ainsi effectué sur les paramètres de stimulation en fonction de la réponse musculaire électrique (onde M) offre la possibilité de stimuler un muscle en prenant en compte une information sur la réaction du muscle à l'électrostimulationThis project aims to develop a new tool for neuromuscular reeducation. Its function is to improve the quality and the duration of muscular strengthening training sessions and training of motor function for patients suffering from muscle deconditioning. A "smart" electromyostimulator using, at the same time, techniques of electrostimulation (EMS) and analysis of electromyography (EMG) allows the control in real time electrical stimulation parameters considering the physiological fatigue of the stimulated muscle. This control, performed on stimulation parameters depending on electrical response of muscles (M wave), allows the muscle stimulation taking into account the muscular reaction to the electrical stimulatio

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NMMGenerator: An automatic neural mass model generator from population graphs

International audienceNeural mass models are among the most popular mathematical models of brain activity, since they enable the rapid simulation of large-scale networks involving different neural types at a spatial scale compatible with electrophysiological experiments (e.g., local field potentials). However, establishing neural mass model (NMM) equations associated with specific neuronal network architectures can be tedious and is an error-prone process, restricting their use to scientists who are familiar with mathematics. In order to overcome this challenge, we have developed a user-friendly software that enables a user to construct rapidly, under the form of a graph, a neuronal network with its populations and connectivity patterns. The resulting graph is then automatically translated into the corresponding set of differential equations, which can be solved and displayed within the same software environment. The software is proposed as open access, and should assist in offering the possibility for a wider audience of scientists to develop NMM corresponding to their specific neuroscience research questions

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Improvement of the Detection of the QRS Complex, T and P Waves in an Electrocardiogram Signal using 12 Leads versus 2 Leads

International audienceThe electrical field potential of the heart recorded from the thoracic part of the human body is depicted by the electrocardiogram signal. This last one is complex and depends on many factors: Position of heart, thickness of the body skin, surface electrode conductivity, acquisition noise and many others. In clinical use, the ECG signal is analysed using twelve leads but in many works in the literature, the analysis methods of the ECG is based on two leads. We present a new method to delineate QRS complexes and T and P waves from electrocardiogram signal. It is based on the continuous wavelet transform. The method is applied on several leads, recorded simultaneously, to improve the localization of the detection. Indeed, if a delineation method is applied on only one lead with some disturbances in it, the result of the delineation could be affected. As the method proposed here merges the result of several leads, the delineation is less affected by disturbances on few leads. The results from this method and from a doctor in medicine are compared. That shows the good ability to separate waves and the enhancement of delineation accuracy when several leads are used

Co-Simulation of Electrical and Mechanical Models of the Uterine Muscle

International audienc