Adaptive Real-Time Decomposition of Electromyogram During Sustained Muscle Activation: A Simulation Study
Adaptive Real-Time Decomposition of Electromyogram During Sustained Muscle Activation: A Simulation Study
复制标题
持续肌肉激活过程中肌电图的自适应实时分解：模拟研究
DOI：
10.1109/tbme.2021.3102947
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发表时间：
2022
影响因子：
4.6
通讯作者：
Hu, Xiaogang
中科院分区：
文献类型：
--
作者：
Zheng, Yang;Hu, Xiaogang
目的：肌电（EMG）的实时分解为运动单元（MU）的活动在神经生理学和人机交互中显示出很好的应用前景。现有的分解方法不能适应EMG信号的随机变化，如动作电位幅度的漂移和MU招聘-去招聘（旋转）模式在长期记录。本研究的目的是开发一种自适应的实时分解方法，适用于长时间的肌肉activation.Methods：我们开发了一个并行双线程计算算法。后端线程使用独立分量分析和卷积核补偿启动并周期性地细化和更新MU信息（分离矩阵）。前端线程执行实时分解。我们在合成的高密度EMG信号上评估了我们的算法，其中MU被零星地招募-解除，MU动作电位幅度随时间漂移。不同的信号-噪声水平也simulated.Results：与没有自适应过程的分解相比，周期性微调和更新的分离矩阵增加了可识别的MU数超过30分钟的信号的3-4倍。增加的MU数在较高的信噪比下更突出。结论：自适应算法可以保持分解性能随时间的变化，允许我们在持续激活期间连续跟踪相同的MU，同时可以将新招募的MU信息添加到现有的分离矩阵中。我们的方法随着时间的推移表现出强大的性能，这有可能纵向评估MU的发射和招聘属性，并提高神经解码性能的神经机器交互。
Objective: Real-time decomposition of electromyogram (EMG) into constituent motor unit (MU) activity has shown promising applications in neurophysiology and human-machine interactions. Existing decomposition methods could not accommodate stochastic variations in EMG signals such as drifts of action potential amplitudes and MU recruitment-derecruitment (rotation) patterns during long-term recordings. The objective of this study was to develop an adaptive real-time decomposition approach suitable for prolonged muscle activation.Methods: We developed a parallel-double-thread computation algorithm. The backend thread initiated and periodically refined and updated the MU information (separation matrix) using independent component analysis and convolution kernel compensation. The frontend thread performed the real-time decomposition. We evaluated our algorithm on synthesized high-density EMG signals, in which MUs were recruited-derecruited sporadically and MU action potentials amplitude drifted over time. Different signal-to-noise levels were also simulated.Results: Compared with the decomposition without the adaptive processes, periodically fine-tuned and updated separation matrix increased identifiable MU number by 3-4 fold over 30-minute of signals. The increased MU number was more prominent at higher signal-to-noise ratios. The decomposition accuracy also increased by up to 10% with greater improvement observed at higher muscle contraction levels.Conclusion: The adaptive algorithm can maintain the decomposition performance over time, allows us to continuously track the same MUs during sustained activation, and, at the same time, can add newly recruited MU information to existing separation matrix.Significance:Our approach showed robust performance over time, which has the potential to longitudinally evaluate MU firing and recruitment properties and improve neural decoding performance for neural-machine interactions.
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影响因子：
4
作者：
Zheng，Yang;Hu，Xiaogang
通讯作者：
Hu，Xiaogang
影响因子：
4
作者：
Michael D Twardowski;Serge H. Roy;Zhi Li;Paola Contessa;G. De Luca;Joshua C. Kline
通讯作者：
Michael D Twardowski;Serge H. Roy;Zhi Li;Paola Contessa;G. De Luca;Joshua C. Kline
DOI：
10.1109/jbhi.2019.2926307
发表时间：
2020-03-01
影响因子：
7.7
作者：
Dai, Chenyun;Hu, Xiaogang
通讯作者：
Hu, Xiaogang
影响因子：
2.5
作者：
Yao, WX;Fuglevand, AJ;Enoka, RM
通讯作者：
Enoka, RM
影响因子：
7.7
作者：
Dai, Chenyun;Hu, Xiaogang
通讯作者：
Hu, Xiaogang