Multi-channel EMG manifestations of upper-extremity muscle coordination imbalance among community-dwelling sarcopenic seniors.

IF 2.9 4区 医学 Q3 ENGINEERING, BIOMEDICAL
Haoru He, Xiaochu Wu, Na Li, Yi Jiang, Jiayuan He, Ning Jiang
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引用次数: 0

Abstract

Background: Sarcopenia is an age-related, insidious, crippling but curable degenerative disease if diagnosed and treated early. However, no accessible and accurate early screening method is available for community settings that does not require specialized personnel. One of the hallmarks of sarcopenia is the pathological changes of muscle fiber type composition and motor unit firing patterns. Surface electromyography (sEMG) may serve as an effective tool for detecting differences between healthy and sarcopenic individuals due to its superior wearability and accessibility compared to other screening methods such as medical imaging and bioimpedance measurements, making it ideal for community-based sarcopenic screening. Our study aims to explore sEMG biomarkers that can be used for screening or diagnosis of sarcopenia.

Results: We collected multi-channel sEMG signals from six forearm muscles of 98 healthy and 55 sarcopenic community-dwelling older adults. Participants performed grasp tasks at 20% and 50% of maximum voluntary contraction (MVC). Hexagons created by various EMG features, normalized with respect to respective MVC, and symmetry analyses were performed to estimate multi-muscle coordination patterns. An innovative index, namely incenter-circumcenter distance of muscle coordination (ICDMC), is proposed to discriminate between the healthy and sarcopenic groups. We utilized non-parametric tests to compare the ICDMC between the two groups, considering a p-value less than 0.05 statistically significant. The results showed that at 20% MVC, ICDMCs from root mean square (RMS), mean absolute value (MAV), slope sign changes (SSC) and wavelength (WL) showed statistically significant differences. More insights of this sEMG manifestation of sarcopenia were revealed by gender- and age-stratifications analyses.

Conclusions: Our results demonstrated that there are clear sEMG manifestations of altered muscle coordination in sarcopenic patients. More consistent force generation patterns were observed in the sarcopenic group, especially at lower contraction intensities. The novel ICDMC can quantify differences between sarcopenic and healthy muscle. These results warrant further research to further develop more accessible sarcopenia screening strategies in community settings based on electrophysiological measurements such as sEMG.

居住在社区的肌肉疏松老年人上肢肌肉协调失衡的多通道肌电图表现。
背景:肌肉疏松症是一种与年龄相关的、隐匿性的、致残性的退行性疾病,但如果及早诊断和治疗,是可以治愈的。然而,在社区环境中还没有无需专业人员即可使用的准确早期筛查方法。肌肉疏松症的特征之一是肌肉纤维类型组成和运动单元发射模式发生病理变化。与医学影像和生物阻抗测量等其他筛查方法相比,表面肌电图(sEMG)具有更佳的可穿戴性和易用性,因此可作为检测健康人与肌肉疏松症患者之间差异的有效工具,是社区肌肉疏松症筛查的理想方法。我们的研究旨在探索可用于筛查或诊断肌肉疏松症的 sEMG 生物标志物:我们收集了 98 名健康和 55 名患有肌肉疏松症的社区老年人前臂六块肌肉的多通道 sEMG 信号。参与者分别在最大自主收缩(MVC)20% 和 50%的情况下完成抓握任务。通过对各种肌电图特征创建的六边形进行归一化处理(与各自的最大自主收缩量相关)和对称性分析,来估计多肌肉协调模式。我们提出了一个创新指标,即肌肉协调的中心-圆心距离(ICDMC),用于区分健康组和肌肉疏松组。我们采用非参数检验来比较两组之间的 ICDMC,认为 p 值小于 0.05 具有统计学意义。结果显示,在 20% MVC 时,均方根(RMS)、平均绝对值(MAV)、斜率符号变化(SSC)和波长(WL)的 ICDMCs 显示出统计学上的显著差异。性别和年龄分层分析揭示了肌肉疏松症的 sEMG 表现:我们的研究结果表明,肌肉疏松症患者的肌肉协调性改变有明显的肌电图表现。肌肉疏松组的发力模式更为一致,尤其是在较低的收缩强度下。新型 ICDMC 可以量化肌肉疏松患者与健康肌肉之间的差异。这些结果值得进一步研究,以便根据 sEMG 等电生理测量方法,在社区环境中进一步开发更方便的肌肉疏松症筛查策略。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
BioMedical Engineering OnLine
BioMedical Engineering OnLine 工程技术-工程:生物医学
CiteScore
6.70
自引率
2.60%
发文量
79
审稿时长
1 months
期刊介绍: BioMedical Engineering OnLine is an open access, peer-reviewed journal that is dedicated to publishing research in all areas of biomedical engineering. BioMedical Engineering OnLine is aimed at readers and authors throughout the world, with an interest in using tools of the physical and data sciences and techniques in engineering to understand and solve problems in the biological and medical sciences. Topical areas include, but are not limited to: Bioinformatics- Bioinstrumentation- Biomechanics- Biomedical Devices & Instrumentation- Biomedical Signal Processing- Healthcare Information Systems- Human Dynamics- Neural Engineering- Rehabilitation Engineering- Biomaterials- Biomedical Imaging & Image Processing- BioMEMS and On-Chip Devices- Bio-Micro/Nano Technologies- Biomolecular Engineering- Biosensors- Cardiovascular Systems Engineering- Cellular Engineering- Clinical Engineering- Computational Biology- Drug Delivery Technologies- Modeling Methodologies- Nanomaterials and Nanotechnology in Biomedicine- Respiratory Systems Engineering- Robotics in Medicine- Systems and Synthetic Biology- Systems Biology- Telemedicine/Smartphone Applications in Medicine- Therapeutic Systems, Devices and Technologies- Tissue Engineering
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