DocumentCode
953010
Title
Spectral Analysis of Motor Unit Action Potentials
Author
Dobrowolski, Andrzej ; Tomczykiewicz, Kazimierz ; Komur, Piotr
Author_Institution
Mil. Univ. of Technol., Warsaw
Volume
54
Issue
12
fYear
2007
Firstpage
2300
Lastpage
2302
Abstract
The statistical processing of electromyographic signal examination performed in the time domain ensures mostly correct classification of pathology; however, because of an ambiguity of most temporal parameter definitions, a diagnosis can include a significant error that strongly depends on the neurologist´s experience. Then, selected temporal parameters are determined for each run, and their mean values are calculated. In the final stage, these mean values are compared with a standard and, including additional clinical information, a diagnosis is given. An inconvenience of this procedure is high time consumption that arises from the necessity of determination of many parameters. Additionally, an ambiguity in determination of basic temporal parameters can cause doubts when parameters found by the physician are compared with standard parameters determined in other research centers. In this paper, we present a definition for spectral discriminant that directly enables a unique diagnosis to be made. An essential advantage of the suggested discriminant is a precise and algorithmically realized definition that enables an objective comparison of examination results obtained by physicians with different experiences or working in different research centers. A suggestion of the standard for selected muscle based on a population of 70 healthy cases is presented in the Results section.
Keywords
electromyography; medical signal processing; neurophysiology; spectral analysis; time-domain analysis; diagnosis; electromyography; motor unit action potentials; muscle; neurologist; spectral analysis; spectral discriminant; statistical processing; time domain; Biomedical measurements; Diseases; Electrodes; Electromyography; Error correction; Military computing; Muscles; Pathology; Shape; Signal processing; Spectral analysis; Statistical analysis; FFT spectrum; Fast Fourier transform (FFT) spectrum; motor unit action potential (MUAP, MUP); quantitative electromyography (QEMG); Action Potentials; Algorithms; Diagnosis, Computer-Assisted; Electromyography; Humans; Motor Neurons; Muscle, Skeletal; Neuromuscular Junction; Signal Processing, Computer-Assisted; Synaptic Transmission;
fLanguage
English
Journal_Title
Biomedical Engineering, IEEE Transactions on
Publisher
ieee
ISSN
0018-9294
Type
jour
DOI
10.1109/TBME.2007.895752
Filename
4359994
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