Abstract
An entropy-based method to quantify the stroke-related change in neuromuscular system was proposed in this study. A stroke patient, a healthy elderly control and a healthy young control were recruited to perform elbow tracking tasks with different dimensions of myoelectric-controlled interface (MCI). The surface electromyographic (sEMG) signals of biceps and triceps were collected and analyzed using refined composite multiscale dispersion entropy (RCMDE). Results showed that at higher time scales, the entropy was less affected by noise than that at lower time scales, and the entropy of the young subject was higher than that of the stroke patient and the elderly subject. Different tracking tasks with different dimensional MCIs induced an observable difference in the complexity of sEMG signals. The RCMDE method might be potential for the evaluation of neurological changes due to aging or diseases.
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