A novel method for automated EMG decomposition and MUAP classification

Authors:
C. D. Katsis;Y. Goletsis;A. Likas;D. I. Fotiadis;I. Sarmas
Affiliations:
Department of Medical Physics, Medical School, University of Ioannina, GR 451 10 Ioannina, Greece and Unit of Medical Technology and Intelligent Information Systems, Department of Computer Science ...;Unit of Medical Technology and Intelligent Information Systems, Department of Computer Science, University of Ioannina, P.O. Box 1186, GR 451 10 Ioannina, Greece and Department of Economics, Unive ...;Unit of Medical Technology and Intelligent Information Systems, Department of Computer Science, University of Ioannina, P.O. Box 1186, GR 451 10 Ioannina, Greece and Biomedical Research Institute- ...;Unit of Medical Technology and Intelligent Information Systems, Department of Computer Science, University of Ioannina, P.O. Box 1186, GR 451 10 Ioannina, Greece and Biomedical Research Institute- ...;Department of Neurosurgery, Medical School, University of Ioannina, GR 451 10, Ioannina, Greece
Venue:
Artificial Intelligence in Medicine
Year:
2006

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Abstract

Objective: This paper proposes a novel method for the extraction and classification of individual motor unit action potentials (MUAPs) from intramuscular electromyographic signals. Methodology: The proposed method automatically detects the number of template MUAP clusters and classifies them into normal, neuropathic or myopathic. It consists of three steps: (i) preprocessing of electromyogram (EMG) recordings, (ii) MUAP detection and clustering and (iii) MUAP classification. Results: The approach has been validated using a dataset of EMG recordings and an annotated collection of MUAPs. The correct identification rate for MUAP clustering is 93, 95 and 92% for normal, myopathic and neuropathic, respectively. Ninety-one percent of the superimposed MUAPs were correctly identified. The obtained accuracy for MUAP classification is about 86%. Conclusion: The proposed method, apart from efficient EMG decomposition addresses automatic MUAP classification to neuropathic, myopathic or normal classes directly from raw EMG signals.