Complexity Analysis of EMG Signals for Patients After Stroke During Robot-Aided Rehabilitation Training Using Fuzzy Approximate Entropy

The paper presents a novel viewpoint to monitor the motor function improvement during a robot-aided rehabilitation training. Eight chronic poststroke subjects were recruited to attend the 20-session training, and in each session, subjects were asked to perform voluntary movements of elbow flexion an...

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Bibliographic Details
Published in:IEEE transactions on neural systems and rehabilitation engineering 2014-09, Vol.22 (5), p.1013-1019
Main Authors: Sun, Rui, Song, Rong, Tong, Kai-yu
Format: Article
Language:English
Subjects:
Adult
Algorithms
Complexity
Complexity theory
Data Interpretation, Statistical
electromyographic (EMG)
Electromyography
Electromyography - methods
Electromyography - statistics & numerical data
Entropy
Female
fuzzy approximate entropy
Fuzzy Logic
Humans
Male
Middle Aged
Muscles
Rehabilitation
robot-aided rehabilitation
Robotics - methods
Robots
stroke
Stroke Rehabilitation
Torque
Training
Upper Extremity - physiopathology
Weightlifting
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Summary:The paper presents a novel viewpoint to monitor the motor function improvement during a robot-aided rehabilitation training. Eight chronic poststroke subjects were recruited to attend the 20-session training, and in each session, subjects were asked to perform voluntary movements of elbow flexion and extension together with the robotic system. The robotic system was continuously controlled by the electromyographic (EMG) signal from the affected triceps. Fuzzy approximate entropy (fApEn) was applied to investigate the complexity of the EMG segment, and maximum voluntary contraction (MVC) during elbow flexion and extension was applied to reflect force generating capacity of the affected muscles. The results showed that the group mean fApEn of EMG signals from triceps and biceps increased significantly after the robot-aided rehabilitation training . There was also significant increase in maximum voluntary flexion and extension torques after the robot-aided rehabilitation training . There was significant correlation between fApEn of agonist and MVC , which implied that the increase of motorneuron number is one of factors that may explain the increase in muscle strength. These findings based on fApEn of the EMG signals expand the existing interpretation of training-induced function improvement in patients after stroke, and help us to understand the neurological change induced by the robot-aided rehabilitation training.
ISSN:1534-4320
1558-0210
DOI:10.1109/TNSRE.2013.2290017