Paper Info
Title
A Stimulus Artifact Removal Technique for SEMG Signal Processing During Functional Electrical Stimulation
Abstract
Goal: The purpose of this study was to design a method for extracting the volitional EMG from recorded surface electromyography (EMG), contaminated by functional electrical stimulation (FES) artifact. Methods: Considering that the FES artifact emerges periodically with rather large amplitude in non-stationary EMG, we designed an adaptive matched filter (AMF) via genetic algorithm (GA) optimization. Both the simulated and real data from seven subjects were processed, using the GA-AMF filter and comb filter, respectively. To test the filtering effect on the EMG, contaminated with FES artifact of different current intensities, the contaminated EMG was simulated by combining the simulation artifact and clean EMG with various FES artifacts to clean EMG ratios. Results: The results show that, in simulation test, compared to the EMG filtered by comb filter, the simulated EMG (p<0.05), filtered by using GA-AMF, had significantly higher correlation coefficient, higher signal to noise ratio (SNR), and lower normalized root mean square error (NRMSE), whereas the real EMG (p<0.05), filtered by using GA-AMF had higher power reduction (PR) than that filtered by using comb filter. The results indicate that GA-AMF can effectively remove FES artifact from the EMG of the stimulated muscle and its adjacent muscle, and the GA-AMF filter performed better than did the comb filter. Conclusion: All these results demonstrate that the GA-AMF filter is capable of extracting volitional EMG from the stimulated muscle and adjacent muscles. Significance: GA-AMF could provide technical support for improving EMG feedback control of FES rehabilitation system.
Year
DOI
Venue
2015
10.1109/TBME.2015.2407834
IEEE Transactions on Biomedical Engineering
Keywords
Field
DocType
Artifact removal, functional electrical stimulation, stimulus artifact, surface EMG
Signal processing,Comb filter,Correlation coefficient,Functional electrical stimulation,Computer science,Signal-to-noise ratio,Filter (signal processing),Electromyography,Electronic engineering,Matched filter
Journal
Volume
Issue
ISSN
PP
99
0018-9294
Citations 
PageRank 
References 
2
0.38
5
Authors
11
Name
Order
Citations
PageRank
Shuang Qiu1223.31
Jing Feng220.38
Rui Xu320.38
Jiapeng Xu462.46
Kun Wang522.07
Feng He6169.45
Hongzhi Qi721.05
Xin Zhao864.16
Peng Zhou9136.25
Lixin Zhang1021.73
Dong Ming1110551.47