文章基本信息

标题：Approach for Classification of Neuromuscular Disorder using EMG Signals
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作者：Amit Kumar Singh ; N K Agrawal ; Sumit Gupta 等
期刊名称：International Journal of Innovative Research in Computer and Communication Engineering
印刷版ISSN：2320-9798
电子版ISSN：2320-9801
出版年度：2017
卷号：5
期号：5
页码：9382
DOI：10.15680/IJIRCCE.2017.0505024
出版社：S&S Publications
摘要：The electromyography (EMG) is an invaluable measurement for the purpose ofassessing muscularactivities. In this paper, a new method for classification of EMGsignals is proposed. The proposed method is based onempirical mode decomposition (EMD) process. In this method, features namely mean, standard deviation, variance andEntropy of the intrinsic mode functions (IMFs) generated by EMD process is used to classification of EMGsignals. Thefeatures measured from the IMFs have been used as a feature for artificial neural network for classification of EMGsignals. The statistical features of IMFs have provided better classification performance. The proposed approachbasedon EMD is better other methods in the literature for classification of EMGsignals.
关键词：Cascaded Kernel Learning Machine (CKLM); Electromyography (EMG); Fractal Dimension (FD) ;Generalized Discriminant Analysis (GDA); Relevance Vector Machines (RVM); Intrinsic Mode Functions (IMFs).