The Experts below are selected from a list of 168 Experts worldwide ranked by ideXlab platform

Jean-yves Hogrel - One of the best experts on this subject based on the ideXlab platform.

  • Motor Unit Conduction Velocity Distribution Estimation From Evoked Motor Responses
    IEEE Transactions on Biomedical Engineering, 2006
    Co-Authors: Isabelle Ledoux, Jacques Duchene, María-teresa García-gonzález, Jean-yves Hogrel
    Abstract:

    Action potentials travel along the muscle fibers with a specific Conduction Velocity that depends on their structural and functional properties. Only the estimation of muscle Conduction Velocity distribution (MCVD) may be able to depict this propagation heterogeneity. Based on the method proposed by Cummins et al. (Electroenceph Clin Neurophysiol, 46:647-658, 1979) to estimate nerve Conduction Velocity distribution (NCVD), the present paper proposes a method that modifies the Cummins' approach to make it suitable for MCVD estimation from electrically evoked motor responses. The MCVD estimation algorithm was first assessed by means of simulated signals in order to control all signal features during the optimization process. Simulations showed that estimated distributions were very close to the true ones when taking into account the specificities of the muscle action potential, due to its generation and extinction (MSE divided by 5 on distribution standard deviation). This method was then applied to real signals. Elicited motor responses were recorded on the biceps brachii of healthy subjects either during repeated maximal stimulations at 20 Hz or during increasing intensity stimulations at 1 Hz. MCVD estimates were used to analyze fatigue and motor unit recruitment processes, respectively.

  • Optimal protocol for muscle fiber Conduction Velocity estimation
    1995
    Co-Authors: Jacques Duchene, Jean-yves Hogrel, Jean-françois Marini
    Abstract:

    Muscle fiber Conduction Velocity (MFCV) is now classically encountered in works dealing with neuromuscular assessment. Unfortunately MFCV estimation often presents a large bias essentially due to non-propagated EMG components. This work proposes some ways to optimize the recording methodology in order to limit this drawback. This optimization is simultaneously realized by a specific electrode configuration and an optimal recording location. The results illustrate how the method can be considered as optimal.

  • Surface EMG simulation for assessment of Conduction Velocity estimation methods
    Proceedings of 18th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 1
    Co-Authors: Jacques Duchene, Jean-yves Hogrel
    Abstract:

    The present work proposes a simple model to simulate electromyographic signal. This model has been developed in order to accurately evaluate the relevance of various processing methods involved in muscular action potential Conduction Velocity estimation. It takes into account experimental, physiological and morphological parameters. Each motor unit is mainly characterized by its recruitment frequency and Conduction Velocity. All parameters are described by statistical distributions with adjustable mean and standard deviation. This simulation model is an efficient tool to assess the signal processing methods used for Conduction Velocity estimation.

R Merletti - One of the best experts on this subject based on the ideXlab platform.

  • noninvasive estimation of motor unit Conduction Velocity distribution using linear electrode arrays
    IEEE Transactions on Biomedical Engineering, 2000
    Co-Authors: Dario Farina, E Fortunato, R Merletti
    Abstract:

    Determining the Conduction Velocity of motor unit action potentials is one of the most important problems in surface electromyography. The estimate of one average Conduction Velocity value depends on a variety of uncontrollable factors. More meaningful information is obtained from the estimation of the distribution of the different delays in the myoelectric signals. A solution to the problem is the separation and characterization of the individual components propagating at different velocities. A technique, based on surface electrode array recording, is proposed to estimate motor unit Conduction Velocity distribution. The method consists in the identification of the single action potentials in the time scale domain (with the continuous wavelet transform) and in the estimation of their Conduction velocities based on the beamforming algorithm. The performances of the technique have been evaluated using simulated and real myoelectric signals. The results demonstrate that the technique Is accurate and reliable. The method may be useful for the diagnosis of neuromuscular disorders, for the monitoring of muscle fatigue and for noninvasive investigation of individual motor units.

Jacques Duchene - One of the best experts on this subject based on the ideXlab platform.

  • Motor Unit Conduction Velocity Distribution Estimation From Evoked Motor Responses
    IEEE Transactions on Biomedical Engineering, 2006
    Co-Authors: Isabelle Ledoux, Jacques Duchene, María-teresa García-gonzález, Jean-yves Hogrel
    Abstract:

    Action potentials travel along the muscle fibers with a specific Conduction Velocity that depends on their structural and functional properties. Only the estimation of muscle Conduction Velocity distribution (MCVD) may be able to depict this propagation heterogeneity. Based on the method proposed by Cummins et al. (Electroenceph Clin Neurophysiol, 46:647-658, 1979) to estimate nerve Conduction Velocity distribution (NCVD), the present paper proposes a method that modifies the Cummins' approach to make it suitable for MCVD estimation from electrically evoked motor responses. The MCVD estimation algorithm was first assessed by means of simulated signals in order to control all signal features during the optimization process. Simulations showed that estimated distributions were very close to the true ones when taking into account the specificities of the muscle action potential, due to its generation and extinction (MSE divided by 5 on distribution standard deviation). This method was then applied to real signals. Elicited motor responses were recorded on the biceps brachii of healthy subjects either during repeated maximal stimulations at 20 Hz or during increasing intensity stimulations at 1 Hz. MCVD estimates were used to analyze fatigue and motor unit recruitment processes, respectively.

  • Optimal protocol for muscle fiber Conduction Velocity estimation
    1995
    Co-Authors: Jacques Duchene, Jean-yves Hogrel, Jean-françois Marini
    Abstract:

    Muscle fiber Conduction Velocity (MFCV) is now classically encountered in works dealing with neuromuscular assessment. Unfortunately MFCV estimation often presents a large bias essentially due to non-propagated EMG components. This work proposes some ways to optimize the recording methodology in order to limit this drawback. This optimization is simultaneously realized by a specific electrode configuration and an optimal recording location. The results illustrate how the method can be considered as optimal.

  • Surface EMG simulation for assessment of Conduction Velocity estimation methods
    Proceedings of 18th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 1
    Co-Authors: Jacques Duchene, Jean-yves Hogrel
    Abstract:

    The present work proposes a simple model to simulate electromyographic signal. This model has been developed in order to accurately evaluate the relevance of various processing methods involved in muscular action potential Conduction Velocity estimation. It takes into account experimental, physiological and morphological parameters. Each motor unit is mainly characterized by its recruitment frequency and Conduction Velocity. All parameters are described by statistical distributions with adjustable mean and standard deviation. This simulation model is an efficient tool to assess the signal processing methods used for Conduction Velocity estimation.

Dario Farina - One of the best experts on this subject based on the ideXlab platform.

  • noninvasive estimation of motor unit Conduction Velocity distribution using linear electrode arrays
    IEEE Transactions on Biomedical Engineering, 2000
    Co-Authors: Dario Farina, E Fortunato, R Merletti
    Abstract:

    Determining the Conduction Velocity of motor unit action potentials is one of the most important problems in surface electromyography. The estimate of one average Conduction Velocity value depends on a variety of uncontrollable factors. More meaningful information is obtained from the estimation of the distribution of the different delays in the myoelectric signals. A solution to the problem is the separation and characterization of the individual components propagating at different velocities. A technique, based on surface electrode array recording, is proposed to estimate motor unit Conduction Velocity distribution. The method consists in the identification of the single action potentials in the time scale domain (with the continuous wavelet transform) and in the estimation of their Conduction velocities based on the beamforming algorithm. The performances of the technique have been evaluated using simulated and real myoelectric signals. The results demonstrate that the technique Is accurate and reliable. The method may be useful for the diagnosis of neuromuscular disorders, for the monitoring of muscle fatigue and for noninvasive investigation of individual motor units.

Felipe Vial - One of the best experts on this subject based on the ideXlab platform.

  • measuring Conduction Velocity distributions in peripheral nerves using neurophysiological techniques
    Clinical Neurophysiology, 2020
    Co-Authors: Felipe Vial, Alexandru V Avram, Giorgio Leodori, Sinisa Pajevic, Peter J Basser, Mark Hallett
    Abstract:

    Abstract Objective To determine how long it takes for neural impulses to travel along peripheral nerve fibers in living humans. Methods A collision test was performed to measure the Conduction Velocity distribution of the ulnar nerve. Two stimuli at the distal and proximal sites were used to produce the collision. Compound muscle or nerve action potentials were recorded to perform the measurements on the motor or mixed nerve, respectively. Interstimulus interval was set at 1–5 ms. A quadri-pulse technique was used to measure the refractory period and calibrate the Conduction time. Results Compound muscle action potential produced by the proximal stimulation started to emerge at the interstimulus interval of about 1.5 ms and increased with the increment in interstimulus interval. Two groups of motor nerve fibers with different Conduction velocities were identified. The mixed nerve showed a wider Conduction Velocity distribution with identification of more subgroups of nerve fibers than the motor nerve. Conclusions The Conduction Velocity distributions in high resolution on a peripheral motor and mixed nerve are different and this can be measured with the collision test. Significance We provided ground truth data to verify the neuroimaging pipelines for the measurements of latency connectome in the peripheral nervous system.