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首页> 外文期刊>Journal of Neuroscience Methods >Extracting burst and tonic components from surface electromyograms in dystonia using adaptive wavelet shrinkage.
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Extracting burst and tonic components from surface electromyograms in dystonia using adaptive wavelet shrinkage.

机译：使用自适应小波收缩从肌张力障碍的表面肌电图提取猝发和强直成分。

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The compound surface electromyograms (EMGs) recorded from patients with dystonia commonly contains superimposed bursting and tonic activity representing various motor symptoms. It is desirable to differentially extract them from the compound EMGs so that different symptoms can be more specifically investigated and different mechanisms revealed. A non-linear denoising approach based on wavelet transformation was investigated by applying soft thresholding to the wavelet coefficients. Thresholds were determined according to three different principles and two models. Different techniques for wavelet shrinkage were investigated for separating burst and tonic activity in the compound EMGs. The combination of Stein's unbiased risk estimate principle with a non-white noise model proved optimal for separating burst and tonic activity. These turned out to be exponentially related; and the temporal relationships between antagonist muscle contractions could now be seen clearly. We conclude that adaptive soft-thresholding wavelet shrinkage provides effective separation of burst and tonic activity in the compound EMG in dystonia. This separation should improve our understanding of the pathophysiology of dystonia.
机译：从肌张力障碍患者记录的复合表面肌电图（EMG）通常包含叠加的爆发和强音活动，代表各种运动症状。希望从复合肌电图中有区别地提取它们，以便可以更具体地研究不同的症状并揭示不同的机制。通过对小波系数应用软阈值，研究了基于小波变换的非线性降噪方法。阈值是根据三种不同的原理和两种模型确定的。研究了用于分离小波收缩的不同技术，以分离复合EMG中的爆发和强音活动。斯坦因的无偏风险估计原理与非白噪声模型的结合被证明是分离突发和强音活动的最佳选择。事实证明，这些指数相关。现在可以清楚地看到拮抗剂肌肉收缩之间的时间关系。我们得出结论，自适应性软阈值小波收缩提供了肌张力障碍复合肌电信号中突发和强直活动的有效分离。这种分离应增进我们对肌张力障碍的病理生理学的理解。

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