A two-stage method for MUAP classification based on EMG decomposition

Authors:
Christos D. Katsis;Themis P. Exarchos;Costas Papaloukas;Yorgos Goletsis;Dimitrios I. Fotiadis;Ioannis 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 ...;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 ...;Department of Biological Applications and Technology, University of Ioannina, GR 451 10 Ioannina, Greece;Department of Economics, 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 and Biomedical Research Institute- ...;Department of Neurosurgery, Medical School, University of Ioannina, GR 451 10, Ioannina, Greece
Venue:
Computers in Biology and Medicine
Year:
2007

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Abstract

A method for the extraction and classification of individual motor unit action potentials (MUAPs) from needle electromyographic signals is presented. The proposed method automatically decomposes MUAPs and classifies them into normal, neuropathic or myopathic using a two-stage feature-based classifier. The method consists of four steps: (i) preprocessing of EMG recordings, (ii) MUAP clustering and detection of superimposed MUAPs, (iii) feature extraction and (iv) MUAP classification using a two-stage classifier. The proposed method employs Radial Basis Function Artificial Neural Networks and decision trees. It requires minimal use of tuned parameters and is able to provide interpretation for the classification decisions. The approach has been validated on real EMG recordings and an annotated collection of MUAPs. The success rate for MUAP clustering is 96%, while the accuracy for MUAP classification is about 89%.