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

C. I. Christodoulou, P. A. Kaplanis, V. Murray, M. S. Pattichis, C. S. Pattichis, T. Kyriakides

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish
Pages (from-to)265-269
Number of pages5
JournalBiomedical Signal Processing and Control
Issue number3
Publication statusPublished - May 2012


  • AM-FM
  • Classification
  • SEMG


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