The C-C method was adopted to analyze the nonlinear characteristics of masseter electromyography (EMG) signals and the chaotic degree by the largest Lyapunov exponent (LLE) of different genders and sides.First,the emb...The C-C method was adopted to analyze the nonlinear characteristics of masseter electromyography (EMG) signals and the chaotic degree by the largest Lyapunov exponent (LLE) of different genders and sides.First,the embedding dimension and the delay time were obtained through this method,then the phase space was reconstructed to resume the chaotic attractor and determine the LLE.The result shows that the trajectory of attractor is denser than Chen's attractor,and the LLE is positive,which means that not only the signal has the character of chaos,but also the chaotic degree of masseter EMG is relatively high.According to the value of the LLE,the chaotic degree of men's masseter EMG is higher than that of women's;when the dentition is normal,the chaotic degree of two sides is almost the same.Then,a conclusion can be deduced that if the LLE of both sides are in great difference,the unilateral mastication is likely to exist,which means that the nonlinear characteristics of masseter EMG can be applied to predict the unilateral mastication.展开更多
Objective:This paper focuses on the research and discussion of the efficacy of electromyographic biofeedback combined with swallowing training on post-stroke dysphagia.Methods:This study randomly sampled and analyzed ...Objective:This paper focuses on the research and discussion of the efficacy of electromyographic biofeedback combined with swallowing training on post-stroke dysphagia.Methods:This study randomly sampled and analyzed 68 patients with post-stroke dysphagia from January 2023 to December 2023,34 cases of swallowing training intervention were grouped as the control group,and 34 cases of electromyography biofeedback combined with swallowing training intervention were grouped as the study group,and the clinical therapeutic effects of the two groups of patients after receiving the two different modes of intervention were compared.Results:The swallowing function of patients in both groups improved,and the VFSS score of patients in the seminar group was significantly higher than that of the control group,indicating that the clinical efficacy of the seminar group was more significant.The nasal feeding tube extraction rate,extraction time,and quality of life scores of the seminar group were better than those of the control group(P<0.05),which is of research value.Conclusion:For patients with post-stroke dysphagia,treatment with electromyography biofeedback combined with swallowing training mode can significantly improve their swallowing function.This effective intervention can not only shorten the time for patients to remove the nasal feeding tube but also help to improve the quality of life of patients,which is worth using.展开更多
The features of electromyographic （EMG） signals were investigated while people walking on different terrains, including up and down slopes, up and down stairs, and during level walking at different speeds, The featu...The features of electromyographic （EMG） signals were investigated while people walking on different terrains, including up and down slopes, up and down stairs, and during level walking at different speeds, The features were used to develop a terrain identification method. The technology can be used to develop an intelligent transfemoral prosthetic limb with terrain identification capability, The EMG signals from 8 hip muscles of 13 healthy persons were recorded as they walked on the different terrains. The signals from the sound side of a transfemoral amputee were also recorded. The features of these signals were obtained using data processing techniques with an identification process developed for the identification of the terrain type. The procedure was simplified by using only the signals from three muscles. The identification process worked well in an intelligent prosthetic knee in a laboratory setting.展开更多
Background Minimally invasive techniques are gaining wide-spread application in lumbar fusion surgery, because they may have advantage over conventional open surgery in approach-related morbidity. This research was ai...Background Minimally invasive techniques are gaining wide-spread application in lumbar fusion surgery, because they may have advantage over conventional open surgery in approach-related morbidity. This research was aimed to evaluate the safety and accuracy of the techniques of minimally invasive transforaminal lumbar interbody fusion by using a computer-assisted spinal navigation system combined with electromyography monitoring. Methods Sixteen patients underwent minimally invasive transforaminal lumbar interbody fusion. A computer-assisted spinal navigation system and electromyography were used for guiding pedicle screw placement. The operative duration, blood loss, complications, and fluoroscopic time were recorded. Clinical outcome was assessed by Visual Analog Scale and Oswestry Disability Index. Radiographic images were obtained to evaluate the accuracy of pedicle screw placement and fusion rates. Results The Visual Analog Scale and Oswestry Disability Index scores were vastly improved postoperatively. A total of 64 pedicle screws were implanted and three were regarded as misplacement by post-operative CT scan. Three screw trajectories were adjusted according to intra-operative stimulus-evoked electromyography monitoring. The average fluoroscopy time in each patient was 31.8 seconds, which equals to 7.9 seconds per pedicle screw. No patients had instrument related neurological complications, infection, implant failure or revision. Successful fusion was found in all patients. Conclusions The combination of navigation system and real-time electromyography monitoring can make the minimally invasive operation more safe and accurate while decreasing radiation exposure time of the medical staff and patient and minimizing the chance and the degree of the pedicle screw misplacement.展开更多
The surface electromyography(sEMG)is one of the basic processing techniques to the gesture recognition because of its inherent advantages of easy collection and non-invasion.However,limited by feature extraction and c...The surface electromyography(sEMG)is one of the basic processing techniques to the gesture recognition because of its inherent advantages of easy collection and non-invasion.However,limited by feature extraction and classifier selection,the adaptability and accuracy of the conventional machine learning still need to promote with the increase of the input dimension and the number of output classifications.Moreover,due to the different characteristics of sEMG data and image data,the conventional convolutional neural network(CNN)have yet to fit sEMG signals.In this paper,a novel hybrid model combining CNN with the graph convolutional network(GCN)was constructed to improve the performance of the gesture recognition.Based on the characteristics of sEMG signal,GCN was introduced into the model through a joint voting network to extract the muscle synergy feature of the sEMG signal.Such strategy optimizes the structure and convolution kernel parameters of the residual network(ResNet)with the classification accuracy on the NinaPro DBl up to 90.07%.The experimental results and comparisons confirm the superiority of the proposed hybrid model for gesture recognition from the sEMG signals.展开更多
文摘The C-C method was adopted to analyze the nonlinear characteristics of masseter electromyography (EMG) signals and the chaotic degree by the largest Lyapunov exponent (LLE) of different genders and sides.First,the embedding dimension and the delay time were obtained through this method,then the phase space was reconstructed to resume the chaotic attractor and determine the LLE.The result shows that the trajectory of attractor is denser than Chen's attractor,and the LLE is positive,which means that not only the signal has the character of chaos,but also the chaotic degree of masseter EMG is relatively high.According to the value of the LLE,the chaotic degree of men's masseter EMG is higher than that of women's;when the dentition is normal,the chaotic degree of two sides is almost the same.Then,a conclusion can be deduced that if the LLE of both sides are in great difference,the unilateral mastication is likely to exist,which means that the nonlinear characteristics of masseter EMG can be applied to predict the unilateral mastication.
文摘Objective:This paper focuses on the research and discussion of the efficacy of electromyographic biofeedback combined with swallowing training on post-stroke dysphagia.Methods:This study randomly sampled and analyzed 68 patients with post-stroke dysphagia from January 2023 to December 2023,34 cases of swallowing training intervention were grouped as the control group,and 34 cases of electromyography biofeedback combined with swallowing training intervention were grouped as the study group,and the clinical therapeutic effects of the two groups of patients after receiving the two different modes of intervention were compared.Results:The swallowing function of patients in both groups improved,and the VFSS score of patients in the seminar group was significantly higher than that of the control group,indicating that the clinical efficacy of the seminar group was more significant.The nasal feeding tube extraction rate,extraction time,and quality of life scores of the seminar group were better than those of the control group(P<0.05),which is of research value.Conclusion:For patients with post-stroke dysphagia,treatment with electromyography biofeedback combined with swallowing training mode can significantly improve their swallowing function.This effective intervention can not only shorten the time for patients to remove the nasal feeding tube but also help to improve the quality of life of patients,which is worth using.
基金Supported by the National Natural Science Foundation of China (No30170242) and the National High-Tech Research and Developmen(863) Program (No. 2001AA320601) of China
文摘The features of electromyographic （EMG） signals were investigated while people walking on different terrains, including up and down slopes, up and down stairs, and during level walking at different speeds, The features were used to develop a terrain identification method. The technology can be used to develop an intelligent transfemoral prosthetic limb with terrain identification capability, The EMG signals from 8 hip muscles of 13 healthy persons were recorded as they walked on the different terrains. The signals from the sound side of a transfemoral amputee were also recorded. The features of these signals were obtained using data processing techniques with an identification process developed for the identification of the terrain type. The procedure was simplified by using only the signals from three muscles. The identification process worked well in an intelligent prosthetic knee in a laboratory setting.
基金This study was supported by a grant from the National Natural Science Foundation of China
文摘Background Minimally invasive techniques are gaining wide-spread application in lumbar fusion surgery, because they may have advantage over conventional open surgery in approach-related morbidity. This research was aimed to evaluate the safety and accuracy of the techniques of minimally invasive transforaminal lumbar interbody fusion by using a computer-assisted spinal navigation system combined with electromyography monitoring. Methods Sixteen patients underwent minimally invasive transforaminal lumbar interbody fusion. A computer-assisted spinal navigation system and electromyography were used for guiding pedicle screw placement. The operative duration, blood loss, complications, and fluoroscopic time were recorded. Clinical outcome was assessed by Visual Analog Scale and Oswestry Disability Index. Radiographic images were obtained to evaluate the accuracy of pedicle screw placement and fusion rates. Results The Visual Analog Scale and Oswestry Disability Index scores were vastly improved postoperatively. A total of 64 pedicle screws were implanted and three were regarded as misplacement by post-operative CT scan. Three screw trajectories were adjusted according to intra-operative stimulus-evoked electromyography monitoring. The average fluoroscopy time in each patient was 31.8 seconds, which equals to 7.9 seconds per pedicle screw. No patients had instrument related neurological complications, infection, implant failure or revision. Successful fusion was found in all patients. Conclusions The combination of navigation system and real-time electromyography monitoring can make the minimally invasive operation more safe and accurate while decreasing radiation exposure time of the medical staff and patient and minimizing the chance and the degree of the pedicle screw misplacement.
文摘目的 观察揿针联合通窍利咽方治疗卒中后吞咽障碍的临床疗效及其对患者吞咽肌群表面肌电信号的影响。方法 将90例卒中后吞咽障碍的患者用随机数字表法平均分为常规组与观察组,每组45例。常规组予常规康复训练及饮食护理,观察组在常规组治疗基础上予揿针联合口服通窍利咽方治疗。比较两组临床疗效,观察两组治疗前后美国国立卫生研究院卒中量表(National Institutes of Health stroke scale,NIHSS)、标准吞咽功能评价量表(standardized swallowing assessment,SSA)、改良曼恩吞咽能力评估量表(Mann assessment of swallowing ability,MASA)和吞咽生活质量量表(swallowing-quality of life,SWAL-QOL)的评分变化,观察两组治疗前后舌骨喉活动度检测指标和吞咽肌群表面肌电信号指标的变化,观察两组治疗前后脑血流动力学指标的变化。比较两组不良反应发生情况。结果 观察组总有效率优于常规组(P<0.05)。治疗后,两组NIHSS和SSA评分均较治疗前下降(P<0.05),MASA和SWAL-QOL评分均较治疗前升高(P<0.05);观察组NIHSS、SSA、MASA和SWAL-QOL评分均优于常规组(P<0.05)。治疗后,观察组舌骨上移、舌骨前移、甲状软骨上移和甲状软骨前移距离均大于治疗前(P<0.05),且舌骨上移和舌骨前移距离均大于常规组(P<0.05)。治疗后,两组空吞与吞咽5 mL温水时吞咽肌群表面肌电信号最大振幅、平均振幅和吞咽时间均较治疗前降低(P<0.05),且观察组上述吞咽肌群表面肌电信号指标均低于常规组(P<0.05)。治疗后,两组患者大脑血管平均血流量、平均血流速度、脉搏波速度、血管特性阻抗和外周阻力均较治疗前改善(P<0.05),且观察组上述脑血流动力学指标均优于常规组(P<0.05)。观察组不良反应发生率低于常规组(P<0.05)。结论 在常规康复训练基础上,揿针联合通窍利咽方治疗卒中后吞咽障碍的临床疗效优于单一中药治疗,可促进神经功能恢复,改善患者吞咽功能和吞咽肌群表面肌电信号,提高脑血流动力学指标,且不良反应发生风险较低。
基金supported by the Development of Sleep Disordered Breathing Detection and Auxiliary Regulation System Project(No.2019I1009)。
文摘The surface electromyography(sEMG)is one of the basic processing techniques to the gesture recognition because of its inherent advantages of easy collection and non-invasion.However,limited by feature extraction and classifier selection,the adaptability and accuracy of the conventional machine learning still need to promote with the increase of the input dimension and the number of output classifications.Moreover,due to the different characteristics of sEMG data and image data,the conventional convolutional neural network(CNN)have yet to fit sEMG signals.In this paper,a novel hybrid model combining CNN with the graph convolutional network(GCN)was constructed to improve the performance of the gesture recognition.Based on the characteristics of sEMG signal,GCN was introduced into the model through a joint voting network to extract the muscle synergy feature of the sEMG signal.Such strategy optimizes the structure and convolution kernel parameters of the residual network(ResNet)with the classification accuracy on the NinaPro DBl up to 90.07%.The experimental results and comparisons confirm the superiority of the proposed hybrid model for gesture recognition from the sEMG signals.
