title: Spike sorting of muscle spindle afferent nerve activity recorded with thin-film intrafascicular electrodes.
abstract: 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.
authors.affiliation: Vision Institute, 17 rue Moreau, 75012 Paris, France. milan.djilas@inserm.fr
authors.forename: Milan
authors.initials: M
authors.lastname: Djilas
authors.forename: Christine
authors.initials: C
authors.lastname: Azevedo-Coste
authors.forename: David
authors.initials: D
authors.lastname: Guiraud
authors.forename: Ken
authors.initials: K
authors.lastname: Yoshida
mesh_terms: D000200:Action Potentials; D000465:Algorithms; D000818:Animals; D004566:Electrodes; D016014:Linear Models; D008954:Models, Biological; D009470:Muscle Spindles / Q000294:innervation* / Q000502:physiology*; D018482:Muscle, Skeletal / Q000294:innervation / Q000502:physiology; D009475:Neurons, Afferent / Q000502:physiology*; D012372:ROC Curve; D011817:Rabbits; D012815:Signal Processing, Computer-Assisted; D013997:Time Factors