Multi-scale AM-FM analysis for the classification of surface electromyographic signals

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• 作者：C.I. Christodoulou^a ; ^{cschr2@ucy.ac.cy} ; P.A. Kaplanis^a ; V. Murray^b ; M.S. Pattichis^b ; C.S. Pattichis^a ; T. Kyriakides^c
• 关键词：AM&ndash ; FM ; SEMG ; Classification
• 刊名：Biomedical Signal Processing and Control
• 出版年：2012
• 期刊代码：2_17468094
• 类别：et
• 出版时间：May, 2012
• 卷：7
• 期：3
• 页码：265-269
• 文件大小：353 K

摘要

In this work, multi-scale amplitude modulation-frequency modulation (AM-FM) features are extracted from surface electromyographic (SEMG) signals and they are used for the classification of neuromuscular disorders. The method is validated on SEMG signals recorded from a total of 40 subjects: 20 normal and 20 abnormal cases (11 myopathy, and 9 neuropathy cases), at 10%, 30%, 50%, 70%and 100%of maximum voluntary contraction (MVC), from the biceps brachii muscle. For the classification, three classifiers are used: (i) the statistical K-nearest neighbor (KNN), (ii) the self-organizing map (SOM) and (iii) the support vector machine (SVM). For all classifiers, the leave-one-out methodology is used to validate the classification of the SEMG signals into normal or abnormal (myopathy or neuropathy). A classification success rate of 78%for the AM-FM features and SVM models was achieved. These results also show that SEMG can be used as a non-invasive alternative to needle EMG for differentiating between normal and abnormal (myopathy, or neuropathy) cases.