A novel method for automated EMG decomposition and MUAP classification.

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.

journal_name

Artif Intell Med

authors

Katsis CD,Goletsis Y,Likas A,Fotiadis DI,Sarmas I

doi

10.1016/j.artmed.2005.09.002

subject

Has Abstract

pub_date

2006-05-01 00:00:00

pages

55-64

issue

1

eissn

0933-3657

issn

1873-2860

pii

S0933-3657(05)00106-5

journal_volume

37

pub_type

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