[Complexity analysis of surface EMG signals].
OBJECTIVE
Since the neurophysiological system is a highly nonlinear dynamic system, nonlinear dynamic information of EMG signals were extracted to describe its characteristics.


METHODS
Two-channel surface EMG signals were extracted and analyzed to reflect the complexity degree of the dynamics of the neurophysiological system.


RESULTS
Complexity measures of four kinds of forearm motions were calculated and compared. They showed a good separability.


CONCLUSIONS
Experimental results proved that this measure, having a simple algorithm, is suitable for short data sets and suitable for real time processing. It provides a new measurable index for both physiological and pathological analysis.
Cai
L Y
LY
Department of Biomedical Engineering, Shanghai Jiao Tong University, China.
Wang
Z Z
ZZ
Zhang
H H
HH
D004576:Electromyography; D005542:Forearm / Q000502:physiology*; D018737:Hand Strength / Q000502:physiology; D009068:Movement / Q000502:physiology*; D018482:Muscle, Skeletal / Q000502:physiology; D017711:Nonlinear Dynamics; D011403:Pronation / Q000502:physiology; D013484:Supination / Q000502:physiology