BACKGROUND Paramyotonia congenita(PMC)stands as a rare sodium channelopaty of skeletal muscle,initially identified by Eulenburg.The identification of PMC often relies on electromyography(EMG),a diagnostic technique.Th...BACKGROUND Paramyotonia congenita(PMC)stands as a rare sodium channelopaty of skeletal muscle,initially identified by Eulenburg.The identification of PMC often relies on electromyography(EMG),a diagnostic technique.The child’s needle EMG unveiled trains of myotonic discharges with notably giant amplitudes,alongside irregular wave trains of myotonic discharges.This distinctive observation had not surfaced in earlier studies.CASE SUMMARY We report the case of a 3-year-old female child with PMC,who exhibited la-ryngeal stridor,muffled speech,myotonia from birth.Cold,exposure to cool water,crying,and physical activity exacerbated the myotonia,which was relieved in warmth,yet never normalized.Percussion myotonia was observable in bilateral biceps.Myotonia symptoms remained unchanged after potassium-rich food consumption like bananas.Hyperkalemic periodic paralysis was excluded.Cranial magnetic resonance imaging yielded normal results.Blood potassium remained within normal range,while creatine kinase showed slight elevation.Exome-wide genetic testing pinpointed a heterozygous mutation on chromosome SCN4A:c.3917G>A(p.G1306E).After a six-month mexiletine regimen,symptoms alleviated.CONCLUSION In this case revealed the two types of myotonic discharges,and had not been documented in other studies.We underscore two distinctive features:Giant-amplitude potentials and irregular waves.展开更多
This study analyzed the difference between using a downward breaststroke kick and a horizontal breaststroke kick in a sample of world class elite swimmers.We compared average muscle activity of the gluteus maximus,qua...This study analyzed the difference between using a downward breaststroke kick and a horizontal breaststroke kick in a sample of world class elite swimmers.We compared average muscle activity of the gluteus maximus,quadriceps femoris(vastus medialis and rectus femoris),hamstring/long head of the biceps femoris,gastrocnemius medialis,rectus abdominal,and erector spinae when using the downward breaststroke kick technique.We find that when this sample of swimmers utilized the downward breaststroke kick,max speed and velocity per stroke increased,measured by 12,788 EMG samples,where the results are highly correlated to duration of the aerodynamic buoyant force in breaststroke kick technique.The increases in performance observed from measuring the world class elite swimmers is highly correlated to the duration of the kick aerodynamic buoyant force.Among this sample of elite swimmers,the longer a swimmer demonstrates a buoyant force breaststroke kick,the lower the time in a 100 breaststroke.展开更多
The human-computer interaction (HCI) is now playing a great role in computer technology. This study introduces an automatic document control technique which is based on the human hand waving movements. The recognition...The human-computer interaction (HCI) is now playing a great role in computer technology. This study introduces an automatic document control technique which is based on the human hand waving movements. The recognition of hand movement is realized according to the surface electromyography (sEMG). A collector is set on the forearm. The sEMG signal is recorded and conveyed to a PC terminal by using wireless Zigbee. An automatic algorithm is developed in order to extract the characteristics of sEMG, recognize the waving movements, and transmit to document control command. The developed human-computer interaction technique can be used as a new gallery for teaching, as well as an assistant tool for disabled person.展开更多
The automatic detection of noisy channels in surface Electromyogram(sEMG)signals,at the time of recording,is very critical in making a noise-free EMG dataset.If an EMG signal contaminated by high-level noise is record...The automatic detection of noisy channels in surface Electromyogram(sEMG)signals,at the time of recording,is very critical in making a noise-free EMG dataset.If an EMG signal contaminated by high-level noise is recorded,then it will be useless and can’t be used for any healthcare application.In this research work,a new machine learning-based paradigm is proposed to automate the detection of low-level and high-level noises occurring in different channels of high density and multi-channel sEMG signals.A modified version of mel fre-quency cepstral coefficients(mMFCC)is proposed for the extraction of features from sEMG channels along with other statistical parameters i-e complexity coef-ficient,hurst exponent,and root mean square.Several state-of-the-art classifiers such as Support Vector Machine(SVM),Ensemble Bagged Trees,Ensemble Sub-space Discriminant,and Logistic Regression are used to automatically identify an EMG channel either bad or good based on these extracted features.Comparison-based analyses of these classifiers have also been considered based on total classi-fication accuracy,prediction speed(observations/sec),and processing time.The proposed method is tested on 320 simulated EMG channels as well as 640 experi-mental EMG channels.SVM is used as our main classifier for the detection of noisy channels which gives a total classification accuracy of 99.4%for simulated EMG channels whereas accuracy of 98.9%is achieved for experimental EMG channels.展开更多
目的:探索术前盆底肌电生理参数对前列腺癌术后尿失禁风险的预测价值。方法:选择2020年1月至2022年10月在北京大学第一医院泌尿外科行根治性前列腺切除术患者的病例资料进行回顾性分析,记录患者的年龄、体重指数(body mass index,BMI)...目的:探索术前盆底肌电生理参数对前列腺癌术后尿失禁风险的预测价值。方法:选择2020年1月至2022年10月在北京大学第一医院泌尿外科行根治性前列腺切除术患者的病例资料进行回顾性分析,记录患者的年龄、体重指数(body mass index,BMI)、国际前列腺症状评分(international prostate symptom score,IPSS)、前列腺特异性抗原(prostate-specific antigen,PSA)水平、Gleason评分、手术方式、是否尿道功能重建、是否淋巴结清扫、性神经是否保留、留置导尿时间、D’Amico风险分级、美国麻醉医师协会(American Society of Anesthesiologists,ASA)评分、查尔森(Charlson)合并症指数、术后随访时间、前列腺体积,以及盆底肌电生理参数(前静息均值、快肌均值和慢肌均值)。通过多因素Logistic回归分析,筛选出影响术后早期尿失禁发生的独立危险因素,并通过计算受试者工作特征曲线(receiver operating characteristic,ROC)下面积,评估盆底肌电参数的预测效能,再利用约登指数(Youden index)并结合临床意义,共同确定术后早期尿失禁发生的最佳临界值。结果:纳入患者271例,术后自主控尿率为81.9%。患者快肌评分为23.5(18.2,31.6)分,慢肌评分为12.5(9.6,17.3)分,179例(66.1%)患者未保留性神经,110例(40.6%)患者进行了尿道功能重建。高龄和盆底快肌评分低被确认为尿失禁发生的独立危险因素。≤60岁患者是≥70岁患者自主控尿率的5.482倍(95%CI:1.532~19.617,P<0.05);患者的盆底快肌评分与尿失禁恢复的关系密切(OR=1.209,95%CI:1.132~1.291,P<0.05)。当术前盆底快肌评分的最佳临界值设定为18.5分时,ROC的敏感度和特异度分别为80.6%和61.2%。结论:术前盆底肌电生理参数对于前列腺癌术后尿失禁风险表现出较好的预测准确性和临床应用性,能够用于前列腺癌术后尿失禁风险的早期识别,其中年龄和盆底快肌评分是重要的预测因子。展开更多
文摘BACKGROUND Paramyotonia congenita(PMC)stands as a rare sodium channelopaty of skeletal muscle,initially identified by Eulenburg.The identification of PMC often relies on electromyography(EMG),a diagnostic technique.The child’s needle EMG unveiled trains of myotonic discharges with notably giant amplitudes,alongside irregular wave trains of myotonic discharges.This distinctive observation had not surfaced in earlier studies.CASE SUMMARY We report the case of a 3-year-old female child with PMC,who exhibited la-ryngeal stridor,muffled speech,myotonia from birth.Cold,exposure to cool water,crying,and physical activity exacerbated the myotonia,which was relieved in warmth,yet never normalized.Percussion myotonia was observable in bilateral biceps.Myotonia symptoms remained unchanged after potassium-rich food consumption like bananas.Hyperkalemic periodic paralysis was excluded.Cranial magnetic resonance imaging yielded normal results.Blood potassium remained within normal range,while creatine kinase showed slight elevation.Exome-wide genetic testing pinpointed a heterozygous mutation on chromosome SCN4A:c.3917G>A(p.G1306E).After a six-month mexiletine regimen,symptoms alleviated.CONCLUSION In this case revealed the two types of myotonic discharges,and had not been documented in other studies.We underscore two distinctive features:Giant-amplitude potentials and irregular waves.
文摘This study analyzed the difference between using a downward breaststroke kick and a horizontal breaststroke kick in a sample of world class elite swimmers.We compared average muscle activity of the gluteus maximus,quadriceps femoris(vastus medialis and rectus femoris),hamstring/long head of the biceps femoris,gastrocnemius medialis,rectus abdominal,and erector spinae when using the downward breaststroke kick technique.We find that when this sample of swimmers utilized the downward breaststroke kick,max speed and velocity per stroke increased,measured by 12,788 EMG samples,where the results are highly correlated to duration of the aerodynamic buoyant force in breaststroke kick technique.The increases in performance observed from measuring the world class elite swimmers is highly correlated to the duration of the kick aerodynamic buoyant force.Among this sample of elite swimmers,the longer a swimmer demonstrates a buoyant force breaststroke kick,the lower the time in a 100 breaststroke.
文摘The human-computer interaction (HCI) is now playing a great role in computer technology. This study introduces an automatic document control technique which is based on the human hand waving movements. The recognition of hand movement is realized according to the surface electromyography (sEMG). A collector is set on the forearm. The sEMG signal is recorded and conveyed to a PC terminal by using wireless Zigbee. An automatic algorithm is developed in order to extract the characteristics of sEMG, recognize the waving movements, and transmit to document control command. The developed human-computer interaction technique can be used as a new gallery for teaching, as well as an assistant tool for disabled person.
文摘为了提高表面肌电信号(surface electromyography,sEMG)的手势分类准确率,通过惯性测量单元(inertial measurement unit,IMU)与采集姿态信号与sEMG的混合信号,提出了GRUBiLSTM双层网络的实时手势分类算法。第1层门控循环单元(gated recurrent unit,GRU)利用能量组合算子特征对混合信号进行突变点检测,定位运动态数据起始点;第2层双向长短时记忆循环神经网络(Bi-directional long short term memory,BiLSTM)使用能量核相图特征对运动态混合信号进行2个方向10种手势的分类。通过离线模型优化,分类算法识别时间低于40 ms,突变点检测精度88.7%以上,手势分类准确率为85%,信息传输率(informationtranslaterate, ITR)达到89.9 bits/min,与基于机器学习的分类算法相比,在准确率与计算效率上具有优势。
基金support from the Deanship of Scientific Research,Najran University.Kingdom of Saudi Arabia,for funding this work under the research groups funding program Grant Code Number(NU/RG/SERC/11/3).
文摘The automatic detection of noisy channels in surface Electromyogram(sEMG)signals,at the time of recording,is very critical in making a noise-free EMG dataset.If an EMG signal contaminated by high-level noise is recorded,then it will be useless and can’t be used for any healthcare application.In this research work,a new machine learning-based paradigm is proposed to automate the detection of low-level and high-level noises occurring in different channels of high density and multi-channel sEMG signals.A modified version of mel fre-quency cepstral coefficients(mMFCC)is proposed for the extraction of features from sEMG channels along with other statistical parameters i-e complexity coef-ficient,hurst exponent,and root mean square.Several state-of-the-art classifiers such as Support Vector Machine(SVM),Ensemble Bagged Trees,Ensemble Sub-space Discriminant,and Logistic Regression are used to automatically identify an EMG channel either bad or good based on these extracted features.Comparison-based analyses of these classifiers have also been considered based on total classi-fication accuracy,prediction speed(observations/sec),and processing time.The proposed method is tested on 320 simulated EMG channels as well as 640 experi-mental EMG channels.SVM is used as our main classifier for the detection of noisy channels which gives a total classification accuracy of 99.4%for simulated EMG channels whereas accuracy of 98.9%is achieved for experimental EMG channels.
文摘目的:探索术前盆底肌电生理参数对前列腺癌术后尿失禁风险的预测价值。方法:选择2020年1月至2022年10月在北京大学第一医院泌尿外科行根治性前列腺切除术患者的病例资料进行回顾性分析,记录患者的年龄、体重指数(body mass index,BMI)、国际前列腺症状评分(international prostate symptom score,IPSS)、前列腺特异性抗原(prostate-specific antigen,PSA)水平、Gleason评分、手术方式、是否尿道功能重建、是否淋巴结清扫、性神经是否保留、留置导尿时间、D’Amico风险分级、美国麻醉医师协会(American Society of Anesthesiologists,ASA)评分、查尔森(Charlson)合并症指数、术后随访时间、前列腺体积,以及盆底肌电生理参数(前静息均值、快肌均值和慢肌均值)。通过多因素Logistic回归分析,筛选出影响术后早期尿失禁发生的独立危险因素,并通过计算受试者工作特征曲线(receiver operating characteristic,ROC)下面积,评估盆底肌电参数的预测效能,再利用约登指数(Youden index)并结合临床意义,共同确定术后早期尿失禁发生的最佳临界值。结果:纳入患者271例,术后自主控尿率为81.9%。患者快肌评分为23.5(18.2,31.6)分,慢肌评分为12.5(9.6,17.3)分,179例(66.1%)患者未保留性神经,110例(40.6%)患者进行了尿道功能重建。高龄和盆底快肌评分低被确认为尿失禁发生的独立危险因素。≤60岁患者是≥70岁患者自主控尿率的5.482倍(95%CI:1.532~19.617,P<0.05);患者的盆底快肌评分与尿失禁恢复的关系密切(OR=1.209,95%CI:1.132~1.291,P<0.05)。当术前盆底快肌评分的最佳临界值设定为18.5分时,ROC的敏感度和特异度分别为80.6%和61.2%。结论:术前盆底肌电生理参数对于前列腺癌术后尿失禁风险表现出较好的预测准确性和临床应用性,能够用于前列腺癌术后尿失禁风险的早期识别,其中年龄和盆底快肌评分是重要的预测因子。