Tutorial: Analysis of central and peripheral motor unit properties from decomposed High-Density surface EMG signals with openhdemg

IF 2 4区 医学 Q3 NEUROSCIENCES Journal of Electromyography and Kinesiology Pub Date : 2023-11-30 DOI:10.1016/j.jelekin.2023.102850
Giacomo Valli , Paul Ritsche , Andrea Casolo , Francesco Negro , Giuseppe De Vito
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Abstract

High-Density surface Electromyography (HD-sEMG) is the most established technique for the non-invasive analysis of single motor unit (MU) activity in humans. It provides the possibility to study the central properties (e.g., discharge rate) of large populations of MUs by analysis of their firing pattern. Additionally, by spike-triggered averaging, peripheral properties such as MUs conduction velocity can be estimated over adjacent regions of the muscles and single MUs can be tracked across different recording sessions. In this tutorial, we guide the reader through the investigation of MUs properties from decomposed HD-sEMG recordings by providing both the theoretical knowledge and practical tools necessary to perform the analyses. The practical application of this tutorial is based on openhdemg, a free and open-source community-based framework for the automated analysis of MUs properties built on Python 3 and composed of different modules for HD-sEMG data handling, visualisation, editing, and analysis. openhdemg is interfaceable with most of the available recording software, equipment or decomposition techniques, and all the built-in functions are easily adaptable to different experimental needs. The framework also includes a graphical user interface which enables users with limited coding skills to perform a robust and reliable analysis of MUs properties without coding.

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教程:从高密度表面肌电信号分解中分析中央和外周运动单元的特性
高密度表面肌电图(HD-sEMG)是最成熟的无创分析人类单运动单元(MU)活动的技术。它提供了一种可能性,通过分析它们的放电模式来研究大量微原子的中心特性(例如放电速率)。此外,通过峰值触发平均,可以估计相邻肌肉区域的微动电位传导速度等周边特性,并且可以在不同的记录过程中跟踪单个微动电位。在本教程中,我们通过提供执行分析所需的理论知识和实用工具,指导读者从分解的HD-sEMG记录中调查MUs属性。本教程的实际应用基于opendemg,这是一个基于Python 3的免费开源社区框架,用于自动分析MUs属性,并由用于HD-sEMG数据处理,可视化,编辑和分析的不同模块组成。Openhdemg可与大多数可用的记录软件,设备或分解技术接口,所有内置功能都很容易适应不同的实验需求。该框架还包括一个图形用户界面,使编码技能有限的用户能够在不编码的情况下对mu属性进行稳健可靠的分析。
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来源期刊
CiteScore
4.70
自引率
8.00%
发文量
70
审稿时长
74 days
期刊介绍: Journal of Electromyography & Kinesiology is the primary source for outstanding original articles on the study of human movement from muscle contraction via its motor units and sensory system to integrated motion through mechanical and electrical detection techniques. As the official publication of the International Society of Electrophysiology and Kinesiology, the journal is dedicated to publishing the best work in all areas of electromyography and kinesiology, including: control of movement, muscle fatigue, muscle and nerve properties, joint biomechanics and electrical stimulation. Applications in rehabilitation, sports & exercise, motion analysis, ergonomics, alternative & complimentary medicine, measures of human performance and technical articles on electromyographic signal processing are welcome.
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