Unsupervised Stochastic Strategies for Robust Detection of Muscle Activation Onsets in Surface Electromyogram.
Unsupervised Stochastic Strategies for Robust Detection of Muscle Activation Onsets in Surface Electromyogram.
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DOI：
10.1109/tnsre.2018.2833742
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发表时间：
2018-06
期刊：
影响因子：
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通讯作者：
Yue GH
中科院分区：
文献类型：
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作者：
Selvan SE;Allexandre D;Amato U;Yue GH
表面肌电图（sEMG）数据提供了有关肌肉功能和神经肌肉疾病的有价值的信息，特别是在人体运动条件下。然而，它们受到试验和受试者的影响，这将对从事精确估计肌肉激活开始的研究人员构成挑战。为此，我们提出了两种无监督的统计方法screepot肘检测（SPE）严重依赖于阈值的选择和更强大的配置文件似然最大化（PLM），避免参数调整，准确地检测肌肉激活发作（MAO）。使用中提供的sEMG数据集和如其中所述创建的模拟sEMG评价这些算法的性能。据报道，这些表面肌电信号是从18名参与者的肱二头肌和股外侧肌收集的，同时分别进行肱二头肌卷曲或膝盖伸展。采集的sEMG信号首先用Teager-Kaiser能量算子进行预处理，然后提供给SPE或PLM或最先进的算法。计算了每种算法估计的MAO时间（毫秒）与中得出的金标准起效时间之间的平均误差和中位误差。PLM变体的结果，即PLM-Laplacian，已被发现与黄金标准具有良好的一致性，即，模拟和实际sEMG数据的绝对中值误差分别为9 ms和21 ms;而根据Wilcoxon秩和检验，其他算法产生的误差在统计学上显著大于PLM-拉普拉斯算子产生的误差。此外，所倡导的方法不需要设定参数，因此具有灵活性，可适应任何应用，这是优于其他几种方法的独特优势。目前正在进行研究，通过施加各种实验条件来进一步验证这种技术。
Surface electromyographic (sEMG) data impart valuable information concerning muscle function and neuromuscular diseases especially under human movement conditions. However, they are subject to trial-wise and subject-wise variations, which would pose challenges for investigators engaged in precisely estimating the onset of muscle activation. To this end, we posited two unsupervised statistical approaches—screeplot elbow detection (SPE) heavily relying on the threshold choice and the more robust profile likelihood maximization (PLM) that obviates parameter tuning—for accurately detecting muscle activation onsets (MAOs). The performance of these algorithms was evaluated using the sEMG dataset provided in and the simulated sEMG created as explained therein. These sEMG signals are reported to have been collected from the biceps brachii and vastus lateralis of 18 participants while performing a biceps curl or knee extension, respectively. The acquired sEMG signals were first preconditioned with the Teager-Kaiser energy operator, and then either supplied to the SPE or to the PLM or to a state of-the-art algorithm. The mean and median errors between the MAO time in milliseconds estimated by each of the algorithms and the gold standard onset time derived in were computed. The outcome of a PLM variant, namely, PLM-Laplacian, has been found to have good agreement with the gold standard, i.e., an absolute median error of 9 ms and 21 ms in the simulated and the actual sEMG data, respectively; whereas, the errors produced by the other algorithms are statistically significantly larger than that incurred by the PLM-Laplacian according to Wilcoxon ranksum test. In addition, the advocated approach does not necessitate parameter settings, lending itself to be flexible and adaptable to any application, which is a unique advantage over several other methods. Research is underway to further validate this technique by imposing various experimental conditions.