A Variance Distribution Model of Surface EMG Signals Based on Inverse Gamma Distribution
A Variance Distribution Model of Surface EMG Signals Based on Inverse Gamma Distribution
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DOI：
10.1109/tbme.2017.2657121
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发表时间：
2017-11-01
影响因子：
4.6
通讯作者：
Tsuji, Toshio
中科院分区：
文献类型：
--
作者：
Hayashi, Hideaki;Furui, Akira;Tsuji, Toshio
目的：本文描述了能够表示肌电图信号方差分布的表面肌电图（EMG）模型的公式。方法：在模型中，肌电图信号基于高斯白噪声过程进行处理，每个方差值的平均值为零。 EMG 信号方差被视为遵循逆伽玛分布的随机变量，允许将噪声表示叠加到该方差上。本文还概述了基于边际似然最大化的方差分布估计。该过程可以使用经过整流和平滑的 EMG 信号来近似，从而可以以较低的计算成本实时确定分布参数。结果：进行了仿真实验来评估使用人工生成的肌电信号进行分布估计的准确性，结果表明所提出的模型的准确性高于基于最大似然估计的准确性。使用真实肌电图数据进行的方差分布分析也表明了方差分布与信号相关噪声之间的关系。结论：本文报告的研究旨在检验所提出的能够表示方差分布的表面肌电图模型的性能以及相关的分布参数估计方法。使用人工和真实肌电图数据进行的实验证明了该模型的有效性。意义：使用所提出的模型估计的方差分布在肌肉力量的估计中表现出潜力。
Objective: This paper describes the formulation of a surface electromyogram (EMG) model capable of representing the variance distribution of EMG signals. Methods: In the model, EMG signals are handled based on a Gaussian white noise process with a mean of zero for each variance value. EMG signal variance is taken as a random variable that follows inverse gamma distribution, allowing the representation of noise superimposed onto this variance. Variance distribution estimation based on marginal likelihood maximization is also outlined in this paper. The procedure can be approximated using rectified and smoothed EMG signals, thereby allowing the determination of distribution parameters in real time at low computational cost. Results: A simulation experiment was performed to evaluate the accuracy of distribution estimation using artificially generated EMG signals, with results demonstrating that the proposed model's accuracy is higher than that of maximum-likelihood-based estimation. Analysis of variance distribution using real EMG data also suggested a relationship between variance distribution and signal-dependent noise. Conclusion: The study reported here was conducted to examine the performance of a proposed surface EMG model capable of representing variance distribution and a related distribution parameter estimation method. Experiments using artificial and real EMG data demonstrated the validity of the model. Significance: Variance distribution estimated using the proposed model exhibits potential in the estimation of muscle force.