Investigating the Stroke- and Aging-Related Changes in Intermuscular Coupling by Refined Composed Multiscale Fuzzy Entropy

Although the entropy algorithm is widely applied in signal complexity analysis, its stability and reliability are limited due to the existence of abnormal signal points. In order to explore the influence of aging and stroke on the human neuromuscular system, refined fuzzy entropy and refined composed multiscale fuzzy entropy were proposed and applied in this study. The simulation results verified that the proposed algorithm improves the stability and the accuracy of entropy estimation. In the experiment, 11 patients after stroke, 10 young controls and 10 aged controls were recruited to perform grasping tasks under different grip strength levels. The grip force signals and surface EMG signals of four muscles on the forearm of the subject’s hemiplegic side (patient) or dominant side (healthy control) were recorded. The results showed that as the time scale increases from 1 to 10, the EMG entropy value: 1) increases at first and then decreases across populations; 2) is larger for the older control subjects than for the younger control subjects originally but then becomes greater for the younger subjects as time increases; 3) is larger for the group of older control subjects than for the stroke survivors in the beginning but then becomes larger in the control subjects. The possible mechanism underlying the changes might be the aging-induced increase in the internal noise of the neuromuscular system and the transfer of effective information on the time scale of EMG signals after stroke. Meanwhile, the influence of aging and stroke on the human neuromuscular system is comprehensively studied by a stable and reliable entropy algorithm, and it contributes to the rehabilitation clinical evaluation of stroke patients.