Analysis of neuromuscular disorders using statistical and entropy metrics on surface EMG

Rok Istenič (Author), Prodromos A. Kaplanis (Author), Constantinos S. Pattichis (Author), Damjan Zazula (Author)

Abstract

This paper introduces the surface electromyogram (EMG) classification system based on statistical and entropy metrics. The system is intended for diagnostic use and enables classification of examined subject as normal, myopathic or neuropathic, regarding to the acquired EMG signals. 39 subjects in total participated in the experiment, 19 normal, 11 myopathic and 9 neuropathic. Surface EMG was recorded using 4-channel surface electrodes on the biceps brachii muscle at isometric voluntary contractions. The recording time was only 5 seconds long to avoid muscle fatigue, and contractions at fiveforce levels were performed, i.e. 10, 30, 50, 70 and 100 % of maximal voluntary contraction. The feature extraction routine deployed the wavelet transform and calculation of the Shannon entropy across all the scales in order to obtain a feature set for each subject. Subjects were classified regarding the extracted features using three machine learning techniques, i.e. decision trees, support vector machines and ensembles of support vector machines. Four 2-class classifications and a 3-class classification were performed. The scored classification rates were the following: 64+-11% for normal/abnormal, 74+-7% for normal/myopathic, 79+-8% for normal/neuropathic, 49+-20% for myopathic/neuropathic, and 63+-8% for normal/myopathic/neuropathic.

Keywords

surface electromyography;neuromuscular disorders;neuropathy;myopathy;EMG signals;signal processing;wavelet transform;metrics;

Data

Language:	English
Year of publishing:	2008
Typology:	1.01 - Original Scientific Article
Organization:	UM FERI - Faculty of Electrical Engineering and Computer Science
UDC:	621.391:61
COBISS:	12385046
ISSN:	1790-5052
Views:	1428
Downloads:	34
Average score:	0 (0 votes)
Metadata:

Other data

Secondary language:	English
Secondary keywords:	elektromiografija;nevrologija;medicina;diagnostika;obdelava signalov;obdelava slik;medicinske slike;valčna transformacija;
URN:	URN:SI:UM:
Pages:	str. 28-35
Volume:	ǂVol. ǂ4
Issue:	ǂiss. ǂ2
Chronology:	Feb. 2008
Keywords (UDC):	applied sciences;medicine;technology;uporabne znanosti;medicina;tehnika;engineering;technology in general;inženirstvo;tehnologija na splošno;mechanical engineering in general;nuclear technology;electrical engineering;machinery;strojništvo;electrical engineering;elektrotehnika;applied sciences;medicine;technology;uporabne znanosti;medicina;tehnika;medical sciences;medicina;
ID:	13697