A two-stage method for MUAP classification based on EMG decomposition.
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%.
Katsis
Christos D
CD
Department of Medical Physics, Medical School, University of Ioannina, GR 451 10 Ioannina, Greece.
Exarchos
Themis P
TP
Papaloukas
Costas
C
Goletsis
Yorgos
Y
Fotiadis
Dimitrios I
DI
Sarmas
Ioannis
I
D000200:Action Potentials / Q000502:physiology*; D000465:Algorithms; D003663:Decision Trees; D004576:Electromyography / Q000379:methods; D006801:Humans; D009119:Muscle Contraction / Q000502:physiology; D016571:Neural Networks, Computer; D009468:Neuromuscular Diseases / Q000175:diagnosis* / Q000503:physiopathology; D012815:Signal Processing, Computer-Assisted