DocumentCode
1559358
Title
Deconvolution estimation of nerve conduction velocity distribution
Author
González-Cueto, José A. ; Parker, Philip A.
Author_Institution
Inst. of Biomed. Eng., New Brunswick Univ., Fredericton, NB, Canada
Volume
49
Issue
2
fYear
2002
Firstpage
140
Lastpage
151
Abstract
A conduction velocity distribution (CVD) estimator that incorporates volume conductor modeling of the nerve-evoked response is introduced in this paper. The CVD estimates are obtained from two compound nerve action potentials (CNAP) recorded at the skin surface. A third channel is introduced in order to assess the estimator performance in the experimental case. The relevance of using an accurate signal model is shown by comparing the performance of the proposed estimator with a previous approach based on a different CNAP model. The performance of the proposed estimator is evaluated for simulated and experimental data. The study assesses signal-to-noise ratio immunity and sensitivity to errors in the model parameters.
Keywords
Legendre polynomials; bioelectric potentials; biomembrane transport; deconvolution; inverse problems; least mean squares methods; medical signal processing; neurophysiology; physiological models; Legendre polynomial; accurate signal model; compound nerve action potentials; constrained optimization problem; deconvolution estimation; error sensitivity; estimator performance; extracellular potential; inverse problem; mean square error; nerve conduction velocity distribution; nerve-evoked response; noninvasive techniques; signal-to-noise ratio immunity; skin surface; surface single fiber action potential; time convolution operation; volume conductor modeling; Conductors; Deconvolution; Diabetes; Nervous system; Neuromuscular; Niobium; Pathology; Signal to noise ratio; Skin; Testing; Action Potentials; Algorithms; Computer Simulation; Humans; Models, Neurological; Models, Theoretical; Neural Conduction; Reproducibility of Results; Stochastic Processes;
fLanguage
English
Journal_Title
Biomedical Engineering, IEEE Transactions on
Publisher
ieee
ISSN
0018-9294
Type
jour
DOI
10.1109/10.979353
Filename
979353
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