A novel method for automated EMG decomposition and MUAP classification

Katsis, C.D.; Goletsis, Y.; Likas, A.; Fotiadis, D.I.; Sarmas, I.

doi:10.1016/j.artmed.2005.09.002

« Previous Next »Artificial Intelligence in Medicine
Volume 37, Issue 1 , Pages 55-64, May 2006

A novel method for automated EMG decomposition and MUAP classification

C.D. Katsis
- Department of Medical Physics, Medical School, University of Ioannina, GR 451 10 Ioannina, Greece
- Unit of Medical Technology and Intelligent Information Systems, Department of Computer Science, University of Ioannina, P.O. Box 1186, GR 451 10 Ioannina, Greece
Y. Goletsis
- Unit of Medical Technology and Intelligent Information Systems, Department of Computer Science, University of Ioannina, P.O. Box 1186, GR 451 10 Ioannina, Greece
- Department of Economics, University of Ioannina, GR 451 10 Ioannina, Greece
A. Likas
- Unit of Medical Technology and Intelligent Information Systems, Department of Computer Science, University of Ioannina, P.O. Box 1186, GR 451 10 Ioannina, Greece
- Biomedical Research Institute—FORTH, GR 451 10 Ioannina, Greece
D.I. Fotiadis
- Unit of Medical Technology and Intelligent Information Systems, Department of Computer Science, University of Ioannina, P.O. Box 1186, GR 451 10 Ioannina, Greece
- Biomedical Research Institute—FORTH, GR 451 10 Ioannina, Greece
- Corresponding author. Tel.: +30 26510 98803; fax: +30 26510 97092.
I. Sarmas
- Department of Neurosurgery, Medical School, University of Ioannina, GR 451 10, Ioannina, Greece

Received 9 May 2005; received in revised form 29 August 2005; accepted 13 September 2005.

Summary

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.

Keywords: Quantitative electromyography, Electromyogram decomposition, Motor unit action potential detection and classification, Support vector machine