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Impact of motor unit action potential components on the motor unit identification from dynamic high-density surface electromyograms

机译:动态高密度表面肌电图对电机单元动作电位组件对电机单元识别的影响

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We assessed the impact of different motor unit action potential (MUAP) components in dynamic muscle contractions on decomposition of high-density surface electromyograms (hdEMG). In particular, hypothesis that nontravelling MUAP components, originating from the tendon regions, are less sensitive to changes in geometry of fusiform muscles than travelling MUAP components has been tested on synthetic monopolar hdEMG signals. The latter have been decomposed by previously introduced Convolution Kernel Compensation (CKC) method, using five different sections of simulated MUAPs for motor unit identification. Accuracy of decomposition results increased significantly when motor units were identified from the nontravelling MUAP components, compared to the results obtained from travelling components. Average motor unit identification sensitivity increased from 67.4%±15.7% to 81.3%±11.3% and false alarm rate decreased from 0.75% ± 1.21% to 0.20% ± 0.24%. Results confirmed that non-travelling MUAP components are discriminative enough to reliably identify motor units from hdEMG and less sensitive to geometric changes of fusiform muscles during dynamic muscle contractions than travelling MUAP components.
机译:我们评估了动态肌肉收缩中不同运动单位动作电位(MUAP)组件对高密度表面肌电图(hdEMG)分解的影响。尤其是,已经在合成单极hdEMG信号上测试了以下假设:源自肌腱区域的非行进式MUAP组件对梭形肌肉的几何形状变化的敏感性低于行进的MUAP组件。后者已通过先前引入的卷积核补偿(CKC)方法进行了分解,使用模拟MUAP的五个不同部分来识别电机单元。当从非行进MUAP组件中识别出电机单元时,与从行进组件获得的结果相比,分解结果的准确性显着提高。平均电机单元识别灵敏度从67.4 \%±15.7 \%提高到81.3 \%±11.3 \%,误报率从0.75 \%±1.21 \%降低到0.20 \%±0.24 \%。结果证实,非行进式MUAP组件具有足够的判别力,可以可靠地从hdEMG识别运动单元,并且在动态肌肉收缩过程中对梭形肌的几何变化不如行进的MUAP组件敏感。

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