Identification of Contaminant Type in Surface Electromyography (EMG) Signals

被引:60
|
作者
McCool, Paul [1 ]
Fraser, Graham D. [2 ]
Chan, Adrian D. C. [2 ]
Petropoulakis, Lykourgos [1 ]
Soraghan, John J. [1 ]
机构
[1] Univ Strathclyde, Dept Elect & Elect Engn, Ctr Excellence Signal & Image Proc, Glasgow G1 1XW, Lanark, Scotland
[2] Carleton Univ, Dept Syst & Comp Engn, Ottawa, ON K1S 5B6, Canada
关键词
Biosignal quality analysis; classification; electromyography (EMG); myoelectric signals; prostheses; SCHEME; MODEL;
D O I
10.1109/TNSRE.2014.2299573
中图分类号
R318 [生物医学工程];
学科分类号
0831 ;
摘要
The ability to recognize various forms of contaminants in surface electromyography (EMG) signals and to ascertain the overall quality of such signals is important in many EMG-enabled rehabilitation systems. In this paper, new methods for the automatic identification of commonly occurring contaminant types in surface EMG signals are presented. Such methods are advantageous because the contaminant type is typically not known in advance. The presented approach uses support vector machines as the main classification system. Both simulated and real EMG signals are used to assess the performance of the methods. The contaminants considered include: 1) electrocardiogram interference; 2) motion artifact; 3) power line interference; 4) amplifier saturation; and 5) additive white Gaussian noise. Results show that the contaminants can readily be distinguished at lower signal to noise ratios, with a growing degree of confusion at higher signal to noise ratios, where their effects on signal quality are less significant.
引用
收藏
页码:774 / 783
页数:10
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