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Classification of surface electromyographic signals using AM-FM features

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5 Author(s)
Christodoulou, C.I. ; Dept. of Comput. Sci., Univ. of Cyprus, Nicosia, Cyprus ; Kaplanis, P.A. ; Murray, V. ; Pattichis, M.S.
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The objective of this study was to evaluate the usefulness of AM-FM features extracted from surface electromyographic (SEMG) signals for the assessment of neuromuscular disorders at different force levels. SEMG signals were recorded from a total of 40 subjects, 20 normal and 20 patients, at 10%, 30%, 50%, 70% and 100% of maximum voluntary contraction (MVC), from the biceps brachii muscle. From the SEMG signals, we extracted the instantaneous amplitude, the instantaneous frequency and the instantaneous phase. For each AM-FM feature their histograms were computed for 32 bins. For the classification, three classifiers were used: (i) the statistical K-nearest neighbour (KNN), (ii) the neural self-organizing map (SOM) and (iii) the neural support vector machine (SVM). For all classifiers the leave-one-out methodology was implemented for the classification of the SEMG signals into normal or pathogenic. The test results reached a classification success rate of 80% when a combination of the three AM-FM features was used.

Published in:

Information Technology and Applications in Biomedicine, 2009. ITAB 2009. 9th International Conference on

Date of Conference:

4-7 Nov. 2009

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