A Double Threshold Adaptive Method for Robust Detection of Muscle Activation Intervals From Surface Electromyographic Signals

The detection of muscle activation intervals is of great significance to the application of gait, gesture, and some other biomedical movements. Surface electromyographic (sEMG) signal can record the electrical activity of muscles effectively. This kind of signal is sometimes, however, corrupted by background noises. Traditionally, the amplitude analysis of the sEMG signal is an alternative approach for the identification of onsets and offsets. In this work, the sEMG signal was analyzed using global-based rapid composite multiscale sample entropy and compared to the other popular algorithms. A double threshold method with an interlocking structure was applied to complete the onsets and offsets detection task. The proposed algorithm was tested in semisynthetic and recorded signals, and it can take advantage of the nonlinear properties of entropy to distinguish the sEMG signal from motion artifacts and tonic spikes. By using the proposed algorithm, the median values of absolute error time for onsets and offsets estimation were 32 and 60 ms, respectively. Meanwhile, the accuracy, false-alarm rate, and missing-alarm rate were 86.1%, 5.7%, and 9.7%, respectively. Our findings suggest that the proposed method can effectively detect muscle activation intervals.

Automated summary

The proposed method utilizes global-based rapid composite multiscale sample entropy to analyze the sEMG signal and applies a double threshold method for the detection of muscle activation intervals. The results demonstrate that the method effectively detects muscle activation intervals.