A Scale Mixture-based Stochastic Model of Surface EMG Signals with Variable Variances
A Scale Mixture-based Stochastic Model of Surface EMG Signals with Variable Variances
复制标题
具有可变方差的表面肌电信号的基于尺度混合的随机模型
DOI：
10.1109/tbme.2019.2895683
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发表时间：
2019
影响因子：
4.6
通讯作者：
and Toshio Tsuji
中科院分区：
文献类型：
--
作者：
Akira Furui;Hideaki Hayashi;and Toshio Tsuji
目的表面肌电图（EMG）信号通常被认为服从高斯分布。然而，在最近的研究中已经报道了与肌肉活动相关的非高斯信号的存在，并且没有一个通用的肌电信号分布模型可以在统一的方案中解释非高斯和高斯分布。方法提出了一种基于尺度混合分布的非高斯肌电模型。在该模型中，某一时刻的肌电信号服从高斯分布，其方差作为服从逆伽马分布的随机变量处理。据此，假定肌电信号的概率分布是均值相同但方差不同的高斯分布。通过边际似然最大化估计肌电图方差分布。结果九名参与者的实验表明，所提出的模型比传统的肌电信号模型更适合于记录的肌电信号。结果还表明，方差分布参数可能反映潜在的运动单元活动。本研究提出了一种基于尺度混合分布的随机肌电图模型，该模型能够表示与肌肉活动相关的非高斯性变化。一系列的实验证明了模型的有效性，并突出了方差分布与肌力之间的关系。提出的模型有助于澄清关于表面肌电信号在统一方案中的概率分布的传统智慧。
ObjectiveSurface electromyogram (EMG) signals have typically been assumed to follow a Gaussian distribution. However, the presence of non-Gaussian signals associated with muscle activity has been reported in recent studies, and there is no general model of the distribution of EMG signals that can explain both non-Gaussian and Gaussian distributions within a unified scheme.MethodsIn this paper, we describe the formulation of a non-Gaussian EMG model based on a scale mixture distribution. In the model, an EMG signal at a certain time follows a Gaussian distribution, and its variance is handled as a random variable that follows an inverse gamma distribution. Accordingly, the probability distribution of EMG signals is assumed to be a mixture of Gaussians with the same mean but different variances. The EMG variance distribution is estimated via marginal likelihood maximization.ResultsExperiments involving nine participants revealed that the proposed model provides a better fit to recorded EMG signals than conventional EMG models. It was also shown that variance distribution parameters may reflect underlying motor unit activity.ConclusionThis study proposed a scale mixture distribution-based stochastic EMG model capable of representing changes in non-Gaussianity associated with muscle activity. A series of experiments demonstrated the validity of the model and highlighted the relationship between the variance distribution and muscle force.SignificanceThe proposed model helps to clarify conventional wisdom regarding the probability distribution of surface EMG signals within a unified scheme.