Suppr超能文献

掩蔽最小二乘平均在具有多次放电的扫描 EMG 记录处理中的应用。

Masked least-squares averaging in processing of scanning-EMG recordings with multiple discharges.

机构信息

Department of Electrical, Electronic and Communication Engineering, Public University of Navarra, 31006, Navarra, Spain.

Bioengineering Group, IdiSNA (Instituto de Investigación Sanitaria de Navarra), 31008, Navarra, Spain.

出版信息

Med Biol Eng Comput. 2020 Dec;58(12):3063-3073. doi: 10.1007/s11517-020-02274-x. Epub 2020 Oct 30.

Abstract

Removing artifacts from nearby motor units is one of the main objectives when processing scanning-EMG recordings. Methods such as median filtering or masked least-squares smoothing (MLSS) can be used to eliminate artifacts in recordings with just one discharge of the motor unit potential (MUP) at each location. However, more effective artifact removal can be achieved if several discharges per position are recorded. In this case, processing usually involves averaging the discharges available at each position and then applying a median filter in the spatial dimension. The main drawback of this approach is that the median filter tends to distort the signal waveform. In this paper, we present a new algorithm that operates on multiple discharges simultaneously and in the spatial dimension. We refer to this algorithm as the multi-masked least-squares smoothing (MMLSS) algorithm: an extension of the MLSS algorithm for the case of multiple discharges. The algorithm is tested using simulated scanning-EMG signals in different recording conditions, i.e., at different levels of muscle contraction and for different numbers of discharges per position. The results demonstrate that the algorithm eliminates artifacts more effectively than any previously available method and does so without distorting the waveform of the signal. Graphical abstract The raw scanning-EMG signal, which can be composed by several discharges of the MU, is processed by the MMLSS algorithm so as to eliminate the artifact interference. Firstly, artifacts are detected for each discharge from the raw signal, obtaining a multi-discharge validity mask that indicates the samples that have been corrupted by artifacts. Secondly, a least-squares smoothing procedure simultaneously operating in the spatial dimension and among the discharges is applied to the raw signal. This second step is performed using only the not contaminated samples according to the validity mask. The resulting MMLSS-processed scanning-EMG signal is clean of artifact interference.

摘要

去除附近运动单元的伪迹是处理扫描肌电图记录的主要目标之一。可以使用中值滤波或掩蔽最小二乘平滑(MLSS)等方法来消除每个记录位置只有一次运动单位电位(MUP)放电的记录中的伪迹。然而,如果记录每个位置的几次放电,可以实现更有效的伪迹去除。在这种情况下,处理通常涉及在每个位置可用的放电进行平均,然后在空间维度中应用中值滤波器。这种方法的主要缺点是中值滤波器往往会扭曲信号波形。在本文中,我们提出了一种新的算法,该算法可以同时在多个放电和空间维度上运行。我们将此算法称为多掩蔽最小二乘平滑(MMLSS)算法:MLSS 算法在多次放电情况下的扩展。该算法在不同的记录条件下使用模拟扫描肌电图信号进行测试,即肌肉收缩程度不同和每个位置的放电次数不同。结果表明,该算法比任何以前可用的方法更有效地消除伪迹,并且不会扭曲信号的波形。

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验