title: A method to improve the estimation of conduction velocity distributions over a short segment of nerve.
abstract: Accurate, noninvasive determination of the distribution of conduction velocities (DCV) among fibers of a peripheral nerve has the potential to improve both clinical diagnoses of pathology and longitudinal studies of the progress of disease or the efficacy of treatments. Current techniques rely on long distances of propagation to increase the amount of temporal dispersion in the compound signals and reduce the relative effect of errors in the forward model. The method described in this paper attempts to reduce errors in DCV estimation through transfer function normalization and, thereby, eliminate the need for long segments of nerve. Compound action potential (CAP) signals are recorded from several, equally spaced electrodes in an array spanning only a 10-cm length of nerve. Relative nerve-to-electrode transfer functions (NETF's) between the nerve and each of the array electrodes are estimated by comparing discrete Fourier transforms of the array signals. NETF's are normalized along the array so that waveform differences can be attributed to the effects of temporal dispersion between recordings, and more accurate DCV estimates can be calculated from the short nerve segment. The method is tested using simulated and real CAP data. DCV estimates are improved for simulated signals. The normalization procedure results in DCV's that qualitatively match those from the literature when used on actual CAP recordings.
authors.affiliation: Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge 02139, USA. mdwells@mit.edu
authors.forename: M D
authors.initials: MD
authors.lastname: Wells
authors.forename: S N
authors.initials: SN
authors.lastname: Gozani
mesh_terms: D000200:Action Potentials / Q000502:physiology*; D000328:Adult; D000465:Algorithms; D003198:Computer Simulation; D004566:Electrodes; D006801:Humans; D008297:Male; D008475:Median Nerve / Q000502:physiology; D008959:Models, Neurological; D009431:Neural Conduction / Q000502:physiology*; D010525:Peripheral Nerves / Q000502:physiology*; D011930:Reaction Time; D012815:Signal Processing, Computer-Assisted