In this study, surface electromyography (sEMG) of the lower limbs of cerebral-palsy (CP) subjects in gait cycle was recorded and its parameters of gait cycle characters were analyzed to assess their clinical severity. Three algorithms, including integrated profile (IP), sample-entropy (SampEN) and smooth nonlinear energy operator (SNEO) algorithm, were applied to calculate the duration of walking sEMG segments in simulated SEMG signals. After that, the efficiency and accuracy were compared among these three algorithms. SNEO was then selected as the optimal algorithm among the three algorithms and employed for real sEMG signal processing of CP subjects. The results indicated that there was no significant difference in the accuracy of sEMG segement detection for the three algorithms. However, the computation speed of SNEO algorithm was much faster than those of the others and thus it was a suitable algorithm for detecting walking sEMG segments of CP subjects. In addition, the positive correlation was found between the clinical severity and the mean duration of walking sEMG segments in CP subjects. The results indicated that there was a significant difference in the three groups of CP subjects with different levels of severity. Our findings showed that the mean duration of walking sEMG segments could be considered as an assistant index to evaluate the clinical severity of CP subjects.
Wearing transfemoral prosthesis is the only way to complete daily physical activity for amputees. Motion pattern recognition is important for the control of prosthesis, especially in the recognizing swing phase and stance phase. In this paper, it is reported that surface electromyography (sEMG) signal is used in swing and stance phase recognition. sEMG signal of related muscles was sampled by Infiniti of a Canadian company. The sEMG signal was then filtered by weighted filtering window and analyzed by height permitted window. The starting time of stance phase and swing phase is determined through analyzing special muscles. The sEMG signal of rectus femoris was used in stance phase recognition and sEMG signal of tibialis anterior is used in swing phase recognition. In a certain tolerating range, the double windows theory, including weighted filtering window and height permitted window, can reach a high accuracy rate. Through experiments, the real walking consciousness of the people was reflected by sEMG signal of related muscles. Using related muscles to recognize swing and stance phase is reachable. The theory used in this paper is useful for analyzing sEMG signal and actual prosthesis control.
The present study was carried out with the surface electromyography signal of subjects during the time when subjects did the exercises of the 6 core stability trainings. We analyzed the different activity level of surface electromyography signal, and finally got various fatigue states of muscles in different exercises. Thirty subjects completed exercises of 6 core stability trainings, which were prone bridge, supine bridge, unilateral bridge (divided into two trainings,i.e. the left and right sides alternatively) and bird-dog (divided into two trainings,i.e. the left and right sides alternatively), respectively. Each exercise was held on for 1 minute and 2 minutes were given to relax between two exercises in this test. We measured both left and right sides of the body’s muscles, which included erector spina, external oblique, rectus abdominis, rectus femoris, biceps femoris, anterior tibial and gastrocnemius muscles. We adopted the frequency domain characteristic value of the surface electromyography signal,i.e. median frequency slope to analyze the muscle fatigue in this study. In the present paper, the results exhibit different fatigue degrees of the above muscles during the time when they did the core stability rehabilitation exercises. It could be concluded that supine bridge and unilateral bridge can cause more fatigue on erector spina muscle, prone bridge caused Gastrocnemius muscle much fatigue and there were statistical significant differences (P<0.05) between prone bridge and other five rehabilitation exercises in the degree of rectus abdominis muscle fatigue. There were no statistical significant differences (P>0.05) between all the left and right sides of the same-named muscles in the median frequency slope during all the exercises of the six core stability trainings,i.e. the degree which the various kinds of rehabilitation exercises effected the left and right side of the same-named muscle had no statistical significant difference (P>0.05). In this research, the conclusion presents quantized guidelines on the effects of core stability trainings on different muscles.
The aim of this study was to design a simple, economic, with high Common Mode Rejection Ratio (CMRR), preamplifier and multi-channel masticatory muscle surface electromyography (sEMG) signal acquisition system assisting to diagnose temporomandibular disorders (TMD). We used the USB interface technology in the EMG data with the aid of the windows to operate system and graphical interface. Eight patients with TMD and eight controls were analyzed separately using this system. In this system, we analyzed sEMG by an optional combination of time domain, frequency domain, time-frequency, several spectral analysis, wavelets and other special algorithms under multi-parameter. Multi-channel sEMG System of Masticatory Muscles is a simple, economic system. It has high sensitivity and specificity. The sEMG signals were changed in patients with TMD. The system would pave the way for diagnosis TMD and help us to assess the treatment effect. A novel and objective method is provided for diagnosis and treatment of oral-maxillofacial disease and functional reconstruction.
Thymoma complicated with polymyositis and myasthenia gravis is a rare case, which can be clearly diagnosed and given symptomatic treatment according to its own diagnostic criteria, imaging and laboratory examinations. This paper reports the clinical data of a thymoma patient with polymyositis and myasthenia gravis admitted to the Seventh Affiliated Hospital of Sun Yat-Sen University, and discusses the possible pathogenesis and treatment methods.
To better analyze the problem of abnormal neuromuscular coupling related to motor dysfunction for stroke patients, the functional coupling of the multichannel electromyography (EMG) were studied and the difference between stroke patients and healthy subjects were further analyzed to explore the pathological mechanism of motor dysfunction after stroke. Firstly, the cross-frequency coherence (CFC) analysis and non-negative matrix factorization (NMF) were combined to construct a CFC-NMF model to study the linear coupling relationship in bands and the nonlinear coupling characteristics in different frequency ratios during elbow flexion and extension movement. Furthermore, the significant coherent area and sum of cross-frequency coherence were respectively calculated to quantitatively describe the intermuscular linear and nonlinear coupling characteristics. The results showed that the linear coupling relationship between multichannel muscles was different in frequency bands and the overall coupling was stronger in low frequency band. The linear coupling strength of the stroke patients was lower than that of the healthy subjects in different frequency bands especially in beta and gamma bands. For the nonlinear coupling, the intermuscular coupling strength of stroke patients in different frequency ratios was significantly lower than that of the healthy subjects, and the coupling strength in the frequency ratio 1∶2 was higher than that in the frequency ratio 1∶3. This method can provide a theoretical basis for exploring the intermuscular coupling mechanism of patients with motor dysfunction.
To quantitatively evaluate the upper-limb spasticity of stroke patients in recovery stage, the relationship between surface electromyography (sEMG) characteristic indexes from biceps brachii and triceps brachii and the spasticity were explored, which provides the electrophysiological basis for clinical rehabilitation. Ten patients with spasticity after stroke were selected to be estimated by modified Ashworth (MAS) assessment and a passive elbow sinusoidal motion experiment was carried out. At the same time, the sEMG of biceps and triceps were recorded. The results shows that the reflex electromyographic threshold could reflect the physiological mechanism of spasticity and had significant correlation with MAS scale which showed that sEMG could be prosperous for the clinical quantitative evaluation of spasticity of stroke patients.
The functional coupling between motor cortex and effector muscles during autonomic movement can be quantified by calculating the coupling between electroencephalogram (EEG) signal and surface electromyography (sEMG) signal. The maximal information coefficient (MIC) algorithm has been proved to be effective in quantifying the coupling relationship between neural signals, but it also has the problem of time-consuming calculations in actual use. To solve this problem, an improved MIC algorithm was proposed based on the efficient clustering characteristics of K-means ++ algorithm to accurately detect the coupling strength between nonlinear time series. Simulation results showed that the improved MIC algorithm proposed in this paper can capture the coupling relationship between nonlinear time series quickly and accurately under different noise levels. The results of right dorsiflexion experiments in stroke patients showed that the improved method could accurately capture the coupling strength of EEG signal and sEMG signal in the specific frequency band. Compared with the healthy controls, the functional corticomuscular coupling (FCMC) in beta (14~30 Hz) and gamma band (31~45 Hz) were significantly weaker in stroke patients, and the beta-band MIC values were positively correlated with the Fugl-Meyers assessment (FMA) scale scores. The method proposed in this study is hopeful to be a new method for quantitative assessment of motor function for stroke patients.
The automatic recognition technology of muscle fatigue has widespread application in the field of kinesiology and rehabilitation medicine. In this paper, we used surface electromyography (sEMG) to study the recognition of leg muscle fatigue during circuit resistance training. The purpose of this study was to solve the problem that the sEMG signals have a lot of noise interference and the recognition accuracy of the existing muscle fatigue recognition model is not high enough. First, we proposed an improved wavelet threshold function denoising algorithm to denoise the sEMG signal. Then, we build a muscle fatigue state recognition model based on long short-term memory (LSTM), and used the Holdout method to evaluate the performance of the model. Finally, the denoising effect of the improved wavelet threshold function denoising method proposed in this paper was compared with the denoising effect of the traditional wavelet threshold denoising method. We compared the performance of the proposed muscle fatigue recognition model with that of particle swarm optimization support vector machine (PSO-SVM) and convolutional neural network (CNN). The results showed that the new wavelet threshold function had better denoising performance than hard and soft threshold functions. The accuracy of LSTM network model in identifying muscle fatigue was 4.89% and 2.47% higher than that of PSO-SVM and CNN, respectively. The sEMG signal denoising method and muscle fatigue recognition model proposed in this paper have important implications for monitoring muscle fatigue during rehabilitation training and exercise.
To better evaluate neuromuscular function of patients with stroke related motor dysfunction, we proposed an effective corticomuscular coherence analysis and coherent significant judgment method. Firstly, the related functional frequency bands in the electroencephalogram (EEG) were extracted via wavelet decomposition. Secondly, coherence were analysed between surface electromyography (sEMG) and sub-bands extracted from EEG. Further more, a coherent significant indicator was defined to quantitatively describe the similarity in certain frequency domain and phase lock activity between EEG and sEMG. Through the analysis of corticomuscular coherence during knee flexion-extension of stroke patients and healthy controls, we found that the stroke patients exhibited significantly lower gamma-band corticomuscular coherence in performing the task with their affected leg, and there was no statistically significant difference between their unaffected lag and the healthy controls, but with the rehabilitation training, the bilateral difference of corticomuscular coherence in patients decreased gradually.