Wavelet analysis for Support Vector Machine classification of motor unit action potentials

The paper presents a new method for neuromuscular disorders diagnosis based on analysis of scalograms determined by the Symlet 4 wavelets technique. Obtained results served for extraction of five features, which, after SVM analysis, were reduced to a single decision parameter allowing assigning the...

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Bibliographic Details
Published in:2010 Annual International Conference of the IEEE Engineering in Medicine and Biology 2010-01, p.4632-4635
Main Authors: Dobrowolski, Andrzej P, Wierzbowski, Mariusz, Tomczykiewicz, Kazimierz
Format: Article
Language:English
Subjects:
Classification algorithms
Electromyography
Muscles
Support vector machines
Training
Wavelet analysis
Wavelet transforms
Online Access:Request full text
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Summary:The paper presents a new method for neuromuscular disorders diagnosis based on analysis of scalograms determined by the Symlet 4 wavelets technique. Obtained results served for extraction of five features, which, after SVM analysis, were reduced to a single decision parameter allowing assigning the investigated cases to one of three groups: myogenic, neurogenic or normal. Software implementation of the method permitted to create a diagnostic tool for EMG investigation aid. The method characterizes high probability of accurate diagnosis of a muscle state with total error of 0.5% - 4 misclassifications out of 780 examined cases.
ISSN:1094-687X
1558-4615
DOI:10.1109/IEMBS.2010.5626480