Entropy-Based Analysis of Electromyography Signal Complexity During Flexion of the Flexor Carpi Radialis Muscle Under Varied Load Conditions

The motor impairments that stroke patients experience as a primary contributor to death and disability also impact their quality of life. Due to its characteristics as complex muscle signals, the Electromyograph (EMG) signal may be one of the markers in rehabilitation and a substitute that may be used to judge the success of rehabilitation. This study aims to analyze the complexity of EMG signals originating from the flexion of the flexor carpi radialis muscle with various weights indicating the rehabilitation state. It is important to monitor the progress of rehabilitation, and it can be done by using complexity analysis. The complexity of the EMG is examined in this work utilizing entropy measurement, namely Approximate Entropy (ApEn), Sample Entropy (SampEn), and Fuzzy Approximate Entropy (fApEn). We concentrated on the flexor carpi radialis muscle using flexion–extension exercises and varying the weight load. This study’s results indicate differences in the complexity of the EMG signal in the flexor carpi radialis when performing flexion–extension movements. Three differences showed a correlation based on the value of ApEN, SampEn, and fApEn. Complexity Analysis using fApEn showed the best correlation at R = 0.9966, which can be used to evaluate the rehabilitation process. The increase in weight was followed by the increase of the fApEn value, indicating that the value can be used to evaluate the rehabilitation process. The use of complexity analysis of EMG signal from flexion movement in flexor carpi radialis with various weights can be used to indicate the progress in the rehabilitation process.