Functional Neuromuscular Stimulation Controlled by Surface Electromyographic Signals Produced by Volitional Activation of the Same Muscle:Adaptive Removal of the Muscle Response from the Recorded EMG-Signal
| Autor: | O.T. Andersen, Fin Biering-Sørensen, S.D. Hansen, S. Sennels |
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| Jazyk: | angličtina |
| Rok vydání: | 1997 |
| Předmět: |
Materials science
medicine.diagnostic_test Electromyography General Engineering Signal Processing Computer-Assisted Stimulation Linear prediction Neuromuscular Diseases Filter (signal processing) Neurophysiology Quadriplegia Models Biological Signal Electric Stimulation Adaptive filter Electronic engineering medicine Humans Artifacts Muscle Skeletal Comb filter Muscle Contraction Biomedical engineering |
| Zdroj: | Sennels, S, Biering-Sørensen, F, Andersen, O T & Hansen, S D 1997, ' Functional Neuromuscular Stimulation Controlled by Surface Electromyographic Signals Produced by Volitional Activation of the Same Muscle : Adaptive Removal of the Muscle Response from the Recorded EMG-Signal ', IEEE Transactions on Rehabilitation Engineering, vol. 5, no. 2, pp. 195-206 . https://doi.org/10.1109/86.593293 Europe PubMed Central |
| DOI: | 10.1109/86.593293 |
| Popis: | In order to use the volitional electromyography (EMG) as a control signal for the stimulation of the same muscle, it is necessary to eliminate the stimulation artifacts and the muscle responses caused by the stimulation. The stimulation artifacts, caused by the electric field in skin and tissue generated by the stimulation current, are relatively easy to eliminate by shutting down the EMG-amplifier at the onset of the stimulation pulses. The muscle response is a nonstationary signal, therefore, an adaptive linear prediction filter is proposed. The filter is implemented and for three filter lengths tested on both simulated and real data. The filter performance is compared with a conventional fixed comb filter. The simulations indicate that the adaptive filter is relatively insensitive to variations in amplitude of the muscle responses, and for all filter lengths produces a good filtering. For variations in shape of the muscle responses and for real data, an increased filter performance can be achieved by increasing the filter length. Using a filter length of up to seven stimulation periods, it is possible to reduce real muscle responses to a level comparable with the background noise. Using the shut-down circuit and the adaptive filter both the stimulation artifacts and the muscle responses can be effectively eliminated from the EMG signal from a stimulated muscle. It is therefore possible to extract the volitional EMG from a partly paralyzed muscle and use it for controlling the stimulation of the same muscle. |
| Databáze: | OpenAIRE |
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