NDLI: Parameter estimation of human nerve C-fibers using matched filtering and multiple hypothesis tracking

Content Provider	IEEE Xplore Digital Library
Author	Hammarberg, B. Forster, C. Torebjork, E.
Copyright Year	1964
Abstract	We describe how multiple-target tracking may be used to estimate conduction velocity changes and recovery constants of human nerve C-fibers. These parameters discriminate different types of C-fibers and pursuing this may promote new insights into differential properties of nerve fiber membranes. Action potentials (APs) were recorded from C-fibers in the peroneal nerve of awake human subjects. The APs were detected by a matched filter constituting a maximum-likelihood constant false-alarm rate detector. Using the multiple-hypothesis tracking method and Kalman filtering, the detected AN (targets) in each trace (scan) were associated to individual nerve fibers (tracks) by their typical conduction latencies in response to electrical stimulation. The measurements were one-dimensional (range only) and the APs were spaced in time with intersecting trajectories. In general, the AP amplitude of each C-fiber differed for different fibers. Amplitude estimation was therefore incorporated into the tracking algorithm to improve the performance. The target trajectory was modeled as an exponential decay with three unknowns. These parameters were estimated iteratively by applying the simplex method on the parameters that enter nonlinearly and the least squares method on the parameters that enter linearly.
Sponsorship	IEEE Engineering in Medicine and Biology Society
Page Count	8
File Size	288731
Starting Page	329
Ending Page	336
File Format	PDF
ISSN	00189294
Volume Number	49
Issue Number	4
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2002-04-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Parameter estimation Humans Matched filters Filtering Target tracking Nerve fibers Biomembranes Maximum likelihood detection Maximum likelihood estimation Detectors
Content Type	Text
Resource Type	Article
Subject	Biomedical Engineering

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