Spike Sorting of Muscle Spindle Afferent Nerve Activity Recorded with Thin-Film Intrafascicular Electrodes

Afferent muscle spindle activity in response to passive muscle stretch was recorded in vivo using thin-film longitudinal intrafascicular electrodes. A neural spike detection and classification scheme was developed for the purpose of separating activity of primary and secondary muscle spindle afferents. The algorithm is based on the multiscale continuous wavelet transform using complex wavelets. The detection scheme outperforms the commonly used threshold detection, especially with recordings having low signal-to-noise ratio. Results of classification of units indicate that the developed classifier is able to isolate activity having linear relationship with muscle length, which is a step towards online model-based estimation of muscle length that can be used in a closed-loop functional electrical stimulation system with natural sensory feedback

Point-process analysis of neural spiking activity of muscle spindles recorded from thin-film longitudinal intrafascicular electrodes

Recordings from thin-film Longitudinal Intra-Fascicular Electrodes (tfLIFE) together with a wavelet-based de-noising and a correlation-based spike sorting algorithm, give access to firing patterns of muscle spindle afferents. In this study we use a point process probability structure to assess mechanical stimulus-response characteristics of muscle spindle spike trains. We assume that the stimulus intensity is primarily a linear combination of the spontaneous firing rate, the muscle extension, and the stretch velocity. By using the ability of the point process framework to provide an objective goodness of fit analysis, we were able to distinguish two classes of spike clusters with different statistical structure. We found that spike clusters with higher SNR have a temporal structure that can be fitted by an inverse Gaussian distribution while lower SNR clusters follow a Poisson-like distribution. The point process algorithm is further able to provide the instantaneous intensity function associated with the stimulus-response model with the best goodness of fit. This important result is a first step towards a point process decoding algorithm to estimate the muscle length and possibly provide closed loop Functional Electrical Stimulation (FES) systems with natural sensory feedback information.National Institutes of Health (U.S.) (Grant R01-HL084502)National Institutes of Health (U.S.) (Grant DP1-OD003646

Interprétation des informations sensorielles des récepteurs du muscle squelettique pour le contrôle externe

The topic of this thesis was the rehabilitation of movement of paralyzed limbs through functional electrical stimulation (FES). The objective of the project was to explore the possibility of using information from sensory nerve fibers of muscle receptors as feedback of the closed-loop control of FES systems using intrafascicular peripheral nerve electrodes.Acute animal experiments were performed to record afferent muscle spindle responses to passive stretch. The recordings were performed using the new thin-film Longitudinal Intra-Fascicular Electrode (tfLIFE), developed by Dr. Ken Yoshida at Aalborg University in Denmark. A first-order model of muscle spindle response to passive muscle stretch was proposed that manages to capture the non-linear properties of the afferent neural activity. Moreover, estimation of muscle state from the recorded multi-channel ENG provided more robust results compared to using single-channel recordings.For the abovementioned model to be usable in a estimator of muscle state, the rate of change of muscle length during movement must have negligible effect on model parameters. A neural spike detection and classification scheme was developed for the purpose of isolating sensory neural activity of muscle receptors having minimal sensitivity to the velocity of muscle motion. The algorithm was based on the multi-scale continuous wavelet transform using complex wavelets. The detection scheme outperforms the commonly used simple threshold detection, especially with recordings having low SNR. Results of classification of units indicate that the developed classifier is able to isolate activity having linear relationship with muscle length, which is a step towards on-line model-based estimation of muscle length that can be used in a closed-loop FES system with natural sensory feedback.One of the main issues limiting the interpretation of ENG data is the low level of the neural signal compared to the level of noise in the recordings. Our hypothesis was that shielding the implant site would help improve signal-to-noise level. Experimental results from a preliminary study indicate that placing a standard cuff electrode around the tfLIFE active sites increases the level of ENG signal in the recordings.Le sujet de cette thèse se situe dans le cadre général de la restauration du mouvement de membres paralysés à travers la stimulation électrique fonctionnelle (FES) implantée. L'objectif du projet était d'explorer la faisabilité d'utiliser les informations issues des fibres nerveuses sensorielles des récepteurs musculaires comme information de retour d'une commande en boucle fermée d'un système FES à travers des électrodes nerveuses périphériques intra fasciculaires. Des expérimentations animales aigues ont été réalisées pour mesurer les réponses afférentes des fuseaux neuromusculaires à des étirements passifs du muscle. Les enregistrements ont été réalisés en utilisant une nouvelle électrode Intra-fasciculaire (tfLIFE), développées par le Dr. Ken Yoshida à l'université d'Aalborg au Danemark. Un modèle du premier ordre de la réponse des fuseaux neuromusculaires à des étirements passifs a été proposé. Ce modèle prend en compte les propriétés non linéaires des activités neurales afférentes. De plus, l'estimation de l'état du muscle à partir d'un enregistrement ENG multicanaux a fourni des résultats plus robustes comparés à un enregistrement monocanal.Pour que le modèle ci-dessus puisse être utilisé pour l'estimation de l'état du muscle, le taux de variation de la longueur du muscle pendant le mouvement doit avoir un effet négligeable sur les paramètres du modèle. Nous avons proposé dans cette thèse une approche pour la détection et la classification de pics dans l'enregisrement neural dans l'objectif d'isoler les activités neurales sensorielles des récepteurs musculaires ayant une sensibilité minimale à la vitesse de l'élongation musculaire. L'algorithme est basé sur la transformée en ondelettes continue multi-échelle utilisant des ondelettes complexes. Le système de détection utilise une simple détection par seuillage, couramment utilisée, particulièrement avec les enregistrements ayant un faible rapport signal sur bruit. Les résultats de classification des unités montrent que la classification développée est capable d'isoler l'activité ayant une relation linéaire avec la longueur du muscle. Ceci constitue une étape vers une estimation, en ligne basée modèle, de la longueur du muscle qui pourra être utilisée dans un système FES en boucle fermée utilisant des informations sensorielles naturelles.Un des principaux problèmes limitant l'interprétation des données ENG est le faible niveau du signal neural par rapport à celui du bruit dans l'enregistrement. Nos hypothèses ont été que le blindage de l'implant aiderait à améliorer le rapport signal sur bruit. Des résultats expérimentaux, issus d'une étude préliminaire que nous avons réalisée, montrent que le placement d'électrodes standards à manchon placées autour du site d'implantation de la tfLIFE augmentait le niveau du signal ENG dans les enregistrements

Interpretation of sensory information from skeletal muscle receptors for external control

Le sujet de cette thèse se situe dans le cadre général de la restauration du mouvement de membres paralysés à travers la stimulation électrique fonctionnelle (FES) implantée. L'objectif du projet était d'explorer la faisabilité d'utiliser les informations issues des fibres nerveuses sensorielles des récepteurs musculaires comme information de retour d'une commande en boucle fermée d'un système FES à travers des électrodes nerveuses périphériques intra fasciculaires. Des expérimentations animales aigues ont été réalisées pour mesurer les réponses afférentes des fuseaux neuromusculaires muscle à des étirements passifs du muscle. Les enregistrements ont été réalisés en utilisant une nouvelle électrode Intra-fasciculaire (tfLIFE), développées par le Dr. Ken Yoshida à l'université d'Aalborg au Danemark. Un modèle du premier ordre de la réponse des fuseaux neuromusculaires à des étirements passifs a été proposé. Ce modèle prend en compte les propriétés non linéaires des activités neurales afférentes. De plus, l'estimation de l'état du muscle à partir d'un enregistrement ENG multicanaux a fourni des résultats plus robustes comparés à un enregistrement monocanal. Pour que le modèle ci-dessus puisse être utilisé pour l'estimation de l'état du muscle, le taux de variation de la longueur du muscle pendant le mouvement doit avoir un effet négligeable sur les paramètres du modèle. Nous avons proposé dans cette thèse une approche pour la détection et la classification de pics dans l'enregisrement neural dans l'objectif d'isoler les activités neurales sensorielles des récepteurs musculaires ayant une sensibilité minimale à la vitesse de l'élongation musculaire. L'algorithme est basé sur la transformée en ondelettes continue multi-échelle utilisant des ondelettes complexes. Le système de détection utilise une simple détection par seuillage, couramment utilisé, particulièrement avec les enregistrements ayant un faible rapport signal sur bruit. Les résultats de classification des unités montrent que la classification développée est capable d'isoler l'activité ayant une relation linéaire avec la longueur du muscle. Ceci constitue une étape vers une estimation, en ligne basée modèle, de la longueur du muscle qui pourra être utilisée dans un système FES en boucle fermée utilisant des informations sensorielles naturelles. Un des principaux problèmes limitant l'interprétation des données ENG est le faible niveau du signal neural par rapport à celui du bruit dans l'enregistrement. Nos hypothèses ont été que le blindage de l'implant aiderait à améliorer le rapport signal sur bruit. Des résultats expérimentaux, issus d'une étude préliminaire que nous avons réalisée, montrent que le placement d'électrodes standards à manchon placées autour du site d'implantation de la tfLIFE augmentait le niveau du signal ENG dans les enregistrementsThe topic of this thesis was the rehabilitation of movement of paralyzed limbs through functional electrical stimulation (FES). The objective of the project was to explore the possibility of using information from sensory nerve fibers of muscle receptors as feedback of the closed-loop control of FES systems using intrafascicular peripheral nerve electrodes. Acute animal experiments were performed to record afferent muscle spindle responses to passive stretch. The recordings were performed using the new thin-film Longitudinal Intra-Fascicular Electrode (tfLIFE), developed by Dr. Ken Yoshida at Aalborg University in Denmark. A first-order model of muscle spindle response to passive muscle stretch was proposed that manages to capture the non-linear properties of the afferent neural activity. Moreover, estimation of muscle state from the recorded multi-channel ENG provided more robust results compared to using single-channel recordings. For the abovementioned model to be usable in a estimator of muscle state, the rate of change of muscle length during movement must have negligible effect on model parameters. A neural spike detection and classification scheme was developed for the purpose of isolating sensory neural activity of muscle receptors having minimal sensitivity to the velocity of muscle motion. The algorithm was based on the multi-scale continuous wavelet transform using complex wavelets. The detection scheme outperformes the commonly used simple threshold detection, especially with recordings having low SNR. Results of classification of units indicate that the developed classifier is able to isolate activity having linear relationship with muscle length, which is a step towards on-line model-based estimation of muscle length that can be used in a closed-loop FES system with natural sensory feedback. One of the main issues limiting the interpretation of ENG data is the low level of the neural signal compared to the level of noise in the recordings. Our hypothesis was that shielding the implant site would help improve signal-to-noise level. Experimental results from a preliminary study we had performed indicate that placing a standard cuff electrode around the tfLIFE active sites increases the level of ENG signal in the recordingsMONTPELLIER-BU Sciences (341722106) / SudocSudocFranceF

Interpretation of muscle spindle afferent nerve response to passive muscle stretch recorded with thin-film longitudinal intrafascicular electrodes

International audienceIn this study, we explored the feasibility of estimating muscle length in passive conditions by interpreting nerve responses from muscle spindle afferents recorded with thin-film longitudinal intrafascicular electrodes. Afferent muscle spindle response to passive stretch was recorded in ten acute rabbit experiments. A newly proposed first-order model of muscle spindle response to passive sinusoidal muscle stretch manages to capture the relationship between afferent neural firing rate and muscle length. We demonstrate that the model can be used to track random motion trajectories with bandwidth from 0.1 to 1 Hz over a range of 4 mm with a muscle length estimation error of 0.3 mm (1.4deg of joint angle). When estimation is performed using four-channel ENG there is a 50% reduction in estimate variation, compared to using single-channel recordings

Improving the signal-to-noise ratio in recordings with thin-film longitudinal intra-fascicular electrodes using shielding cuffs

International audienceAn elegant solution to the problem of instrumenting paralyzed limbs with artificial sensors for use with closedloop FES systems is to use natural sensors, such as muscle afferent activity as feedback for the artificial controller. Longitudinal intra-fascicular electrodes (LIFEs) are electrodes that have shown promise in this application. As a peripheral nerve interface, they are designed to be placed inside the peripheral nerve, but near potentially active muscles and the stimulating electrode. Artefacts from EMG and stimulation remain limiting factors in signal acquisition. Here we present a technique for improving the signal-to-noise ratio which consists of wrapping a shield around the implant site of the recording electrode. Preliminary results obtained during in-vivo experiments suggest that the shielding increases the level of the neural signal in the recordings

Improving the signal-to-noise ratio on recordings with thin-film longitudinal intrafascicular electrodes using shielding cuffs

International audienceAn elegant solution to the problem of instrumenting paralyzed limbs with artificial sensors for use with closedloop FES systems is to use natural sensors, such as muscle afferent activity as feedback for the artificial controller. Longitudinal intra-fascicular electrodes (LIFEs) are electrodes that have shown promise in this application. As a peripheral nerve interface, they are designed to be placed inside the peripheral nerve, but near potentially active muscles and the stimulating electrode. Artefacts from EMG and stimulation remain limiting factors in signal acquisition. Here we present a technique for improving the signal-to-noise ratio which consists of wrapping a shield around the implant site of the recording electrode. Preliminary results obtained during in-vivo experiments suggest that the shielding increases the level of the neural signal in the recordings

Wavelet-Based Spike Sorting of Muscle Spindle Afferent Nerve Activity Recorded With Thin-Film Intrafascicular Electrodes

International audienceThe continuous complex wavelet transform offers a convenient framework for neural spike sorting. Results show that wavelet-based neural spike detection outperforms simple threshold detection, especially with signals with low signal to noise ratio. Classification of action potentials using their signatures in wavelet space performed as well as a classifier based upon principal components analysis, and better than a classifier based upon template matching. Applied on experimental intrafascicular recordings of muscle spindle afferent nerve response to passive muscle stretch, the spike sorting algorithm manages to isolate afferent activity of units having a linear relationship between neural firing rate and muscle length, an important step towards a model-based estimator of muscle length