A Novel Validation Approach for High-Density Surface EMG Decomposition in Motor Neuron Disease.
A Novel Validation Approach for High-Density Surface EMG Decomposition in Motor Neuron Disease.
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运动神经元疾病高密度表面肌电图分解的新验证方法
DOI：
10.1109/tnsre.2018.2836859
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发表时间：
2018-06
期刊：
影响因子：
--
通讯作者：
Zhou P
中科院分区：
文献类型：
--
作者：
Chen M;Zhang X;Zhou P
本文提出了一种新的双源方法来验证高密度表面肌电图（EMG）分解的性能。该方法是利用肌萎缩性侧索硬化症（ALS）的表面肌电特征开发的。ALS患者高密度表肌电信号数据可分为稀疏数据集和干涉数据集，稀疏数据集由专家目测分解，干涉数据集由表肌电信号分解算法独立分解。两个数据集的分解结果的一致性可以量化，以评估表面肌电分解的性能。新的验证方法是对最近开发的用于高密度表面肌电分解的自动渐进式FastICA剥离（APFP）方法进行的。应用APFP框架自动分解肌萎缩侧索硬化症患者第一背骨间肌高密度表面肌电信号。从干扰数据集和稀疏数据集独立分解的常见运动单元平均匹配率为99.18%±1.11%。肌萎缩侧索硬化症表面肌电信号的特征也有助于一步一步地说明高密度表面肌电信号分解的APFP框架。本文提出的新方法可以补充传统的双源验证，用于从实验信号中评估分解运动单元的准确性，这对于表面肌电分解方法的发展至关重要。
This paper presents a novel two-source approach for validating the performance of high-density surface electromyogram (EMG) decomposition. The approach was developed taking advantage of surface EMG characteristics of amyotrophic lateral sclerosis (ALS). High-density surface EMG data from ALS patients can be divided to the sparse data set and the interference data set, with the former decomposed by expert visual inspection while the latter independently decomposed by the surface EMG decomposition algorithm. The agreement of the decomposition yields from the two data sets can be quantified for evaluating the surface EMG decomposition performance. The novel validation approach was performed for a recently developed method called automatic progressive FastICA peel-off (APFP) for high-density surface EMG decomposition. The APFP framework was used to automatically decompose high-density surface EMG signals recorded from the first dorsal interosseous muscle of ALS subjects. The common motor units independently decomposed from the interference data set and the sparse data set demonstrated an average matching rate of 99.18% ± 1.11%. The characteristics of the ALS surface EMG also facilitate a step by step illustration of the APFP framework for high-density surface EMG decomposition. The novel approach presented in this paper can supplement conventional two-source validation for accuracy assessment of decomposed motor units from experimental signals, which is essential for development of surface EMG decomposition methods.