EMG Signal Classification by Extreme Learning Machine
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Date
2013
Journal Title
Journal ISSN
Volume Title
Publisher
IEEE
Access Rights
info:eu-repo/semantics/closedAccess
Abstract
From disease detection to action assessment EMG signals are used variety of field. Miscellaneous studies have been conducted toward analysis of EMG signals. In this study some statistical features of signal were derived, the best evocative features were selected via Linear Discriminant Analysis (LDA) and feature vectors were constructed. This analytic feature vectors were classified through Extreme Learning Machine (ELM). 8 channel EMG signals recorded from 10 normal and 10 aggressive actions were used as an example. By cross-comparison of the obtained results to the ones obtained via various feature identifying methods (AR coefficients, wavelet energy and entropy) and classification methods (NB, SVM, LR, ANN, PART, Jrip, J48 and LMT) the success of the proposed method was determined.
Description
21st Signal Processing and Communications Applications Conference (SIU) -- APR 24-26, 2013 -- CYPRUS
Keywords
EMG, Discriminant Analysis, Extreme Learning Machine, statistical parameters
Journal or Series
2013 21st Signal Processing and Communications Applications Conference (Siu)
WoS Q Value
N/A
Scopus Q Value
N/A
