복합근전도로부터 자발성분과 유발성분을 추출하기 위한 알고리즘 개발
A New Algorithm for Extracting Voluntary Component and Evoked Component from Mixed EMG
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This study was designed to develop a new algorithm to extract the voluntary EMG and the evoked EMG from a mixed EMG generated when the muscle is stimulated both voluntarily and by electrical stimulation in the FES system. The proposed parallel filter algorithm consists of three phases: (1) Fourier transform of the mixed EMG, (2) multiplication of the transformed signal to two frequency functions, and (3) inverse Fourier transform. Four incomplete spinal cord injured patients participated in the experiments to evaluate the algorithm by measuring the knee extensor torque and the EMG signals from the quadriceps. Two functions of the algorithms were evaluated: (1) extraction of the evoked EMG and (2) the voluntary EMG from the mixed EMG. The results showed that the algorithm enabled us to separate the two EMG components in real time from the mixed EMG. The algorithm can and will be used for estimating the voluntary torque and the evoked torque independently through an artificial neural network based on the two EMG components, and also for generating a trigger signal to control the on/off time of the FES system
Abstract
This study was designed to develop a new algorithm to extract the voluntary EMG and the evoked EMG from a mixed EMG generated when the muscle is stimulated both voluntarily and by electrical stimulation in the FES system. The proposed parallel filter algorithm consists of three phases: (1) Fourier transform of the mixed EMG, (2) multiplication of the transformed signal to two frequency functions, and (3) inverse Fourier transform. Four incomplete spinal cord injured patients participated in the experiments to evaluate the algorithm by measuring the knee extensor torque and the EMG signals from the quadriceps. Two functions of the algorithms were evaluated: (1) extraction of the evoked EMG and (2) the voluntary EMG from the mixed EMG. The results showed that the algorithm enabled us to separate the two EMG components in real time from the mixed EMG. The algorithm can and will be used for estimating the voluntary torque and the evoked torque independently through an artificial neural network based on the two EMG components, and also for generating a trigger signal to control the on/off time of the FES system
참고문헌 (23)
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