NDLI: Comparative study of PCA in classification of multichannel EMG signals

Content Provider	Springer Nature Link
Author	Geethanjali, P.
Copyright Year	2015
Abstract	Electromyographic (EMG) signals are abundantly used in the field of rehabilitation engineering in controlling the prosthetic device and significantly essential to find fast and accurate EMG pattern recognition system, to avoid intrusive delay. The main objective of this paper is to study the influence of Principal component analysis (PCA), a transformation technique, in pattern recognition of six hand movements using four channel surface EMG signals from ten healthy subjects. For this reason, time domain (TD) statistical as well as auto regression (AR) coefficients are extracted from the four channel EMG signals. The extracted statistical features as well as AR coefficients are transformed using PCA to 25, 50 and 75 % of corresponding original feature vector space. The classification accuracy of PCA transformed and non-PCA transformed TD statistical features as well as AR coefficients are studied with simple logistic regression (SLR), decision tree (DT) with J48 algorithm, logistic model tree (LMT), k nearest neighbor (kNN) and neural network (NN) classifiers in the identification of six different movements. The Kruskal–Wallis (KW) statistical test shows that there is a significant reduction (P < 0.05) in classification accuracy with PCA transformed features compared to non-PCA transformed features. SLR with non-PCA transformed time domain (TD) statistical features performs better in accuracy and computational power compared to other features considered in this study. In addition, the motion control of three drives for six movements of the hand is implemented with SLR using TD statistical features in off-line with TMSLF2407 digital signal controller (DSC).
Starting Page	331
Ending Page	343
Page Count	13
File Format	PDF
ISSN	01589938
Journal	Australasian Physics & Engineering Sciences in Medicine
Volume Number	38
Issue Number	2
e-ISSN	18795447
Language	English
Publisher	Springer Netherlands
Publisher Date	2015-04-10
Publisher Place	Dordrecht
Access Restriction	Subscribed
Subject Keyword	EMG Feature extraction Classification Principal component analysis Digital signal controller Biomedicine general Biophysics and Biological Physics Medical and Radiation Physics Biomedical Engineering
Content Type	Text
Resource Type	Article
Subject	Radiology, Nuclear Medicine and Imaging Physics and Astronomy Biophysics Biomedical Engineering

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