用于表面肌电图分解的快速梯度卷积核补偿方法

IF 2 4区 医学 Q3 NEUROSCIENCES Journal of Electromyography and Kinesiology Pub Date : 2024-03-04 DOI:10.1016/j.jelekin.2024.102869
Chuang Lin , Ziwei Cui , Chen Chen , Yanhong Liu , Chen Chen , Ning Jiang
{"title":"用于表面肌电图分解的快速梯度卷积核补偿方法","authors":"Chuang Lin ,&nbsp;Ziwei Cui ,&nbsp;Chen Chen ,&nbsp;Yanhong Liu ,&nbsp;Chen Chen ,&nbsp;Ning Jiang","doi":"10.1016/j.jelekin.2024.102869","DOIUrl":null,"url":null,"abstract":"<div><p>Decomposition of EMG signals provides the decoding of motor unit (MU) discharge timings. In this study, we propose a fast gradient convolution kernel compensation (fgCKC) decomposition algorithm for high-density surface EMG decomposition and apply it to an offline and real-time estimation of MU spike trains. We modified the calculation of the cross-correlation vectors to improve the calculation efficiency of the gradient convolution kernel compensation (gCKC) algorithm. Specifically, the new fgCKC algorithm considers the past gradient in addition to the current gradient. Furthermore, the EMG signals are divided by sliding windows to simulate real-time decomposition, and the proposed algorithm was validated on simulated and experimental signals. In the offline decomposition, fgCKC has the same robustness as gCKC, with sensitivity differences of 2.6 ± 1.3 % averaged across all trials and subjects. Nevertheless, depending on the number of MUs and the signal-to-noise ratio of signals, fgCKC is approximately 3 times faster than gCKC. In the real-time part, the processing only needed 240 <em>ms</em> average per window of EMG signals on a regular personal computer (IIntel(R) Core(TM) i5-12490F 3 GHz, 16 GB memory). These results indicate that fgCKC achieves real-time decomposition by significantly reducing processing time, providing more possibilities for non-invasive neuronal behavior research.</p></div>","PeriodicalId":56123,"journal":{"name":"Journal of Electromyography and Kinesiology","volume":"76 ","pages":"Article 102869"},"PeriodicalIF":2.0000,"publicationDate":"2024-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A fast gradient convolution kernel compensation method for surface electromyogram decomposition\",\"authors\":\"Chuang Lin ,&nbsp;Ziwei Cui ,&nbsp;Chen Chen ,&nbsp;Yanhong Liu ,&nbsp;Chen Chen ,&nbsp;Ning Jiang\",\"doi\":\"10.1016/j.jelekin.2024.102869\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Decomposition of EMG signals provides the decoding of motor unit (MU) discharge timings. In this study, we propose a fast gradient convolution kernel compensation (fgCKC) decomposition algorithm for high-density surface EMG decomposition and apply it to an offline and real-time estimation of MU spike trains. We modified the calculation of the cross-correlation vectors to improve the calculation efficiency of the gradient convolution kernel compensation (gCKC) algorithm. Specifically, the new fgCKC algorithm considers the past gradient in addition to the current gradient. Furthermore, the EMG signals are divided by sliding windows to simulate real-time decomposition, and the proposed algorithm was validated on simulated and experimental signals. In the offline decomposition, fgCKC has the same robustness as gCKC, with sensitivity differences of 2.6 ± 1.3 % averaged across all trials and subjects. Nevertheless, depending on the number of MUs and the signal-to-noise ratio of signals, fgCKC is approximately 3 times faster than gCKC. In the real-time part, the processing only needed 240 <em>ms</em> average per window of EMG signals on a regular personal computer (IIntel(R) Core(TM) i5-12490F 3 GHz, 16 GB memory). These results indicate that fgCKC achieves real-time decomposition by significantly reducing processing time, providing more possibilities for non-invasive neuronal behavior research.</p></div>\",\"PeriodicalId\":56123,\"journal\":{\"name\":\"Journal of Electromyography and Kinesiology\",\"volume\":\"76 \",\"pages\":\"Article 102869\"},\"PeriodicalIF\":2.0000,\"publicationDate\":\"2024-03-04\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Electromyography and Kinesiology\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S1050641124000130\",\"RegionNum\":4,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"NEUROSCIENCES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Electromyography and Kinesiology","FirstCategoryId":"3","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1050641124000130","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"NEUROSCIENCES","Score":null,"Total":0}
引用次数: 0

摘要

肌电信号的分解提供了运动单元(MU)放电时序的解码。在这项研究中,我们提出了一种用于高密度表面肌电信号分解的快速梯度卷积核补偿(fgCKC)分解算法,并将其应用于离线和实时估算 MU 尖峰列车。我们修改了交叉相关向量的计算方法,以提高梯度卷积核补偿(gCKC)算法的计算效率。具体来说,新的 fgCKC 算法除了考虑当前梯度外,还考虑了过去的梯度。此外,用滑动窗口分割肌电信号以模拟实时分解,并在模拟和实验信号上验证了所提出的算法。在离线分解中,fgCKC 与 gCKC 具有相同的鲁棒性,所有试验和受试者的灵敏度差异平均为 2.6 ± 1.3 %。不过,根据单元数和信号的信噪比,fgCKC 比 gCKC 快约 3 倍。在实时部分,在一台普通个人电脑(Intel(R) Core(TM) i5-12490F 3 GHz,16 GB 内存)上处理每个窗口的肌电信号平均只需要 240 次。这些结果表明,fgCKC 通过大幅缩短处理时间实现了实时分解,为无创神经元行为研究提供了更多可能性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
A fast gradient convolution kernel compensation method for surface electromyogram decomposition

Decomposition of EMG signals provides the decoding of motor unit (MU) discharge timings. In this study, we propose a fast gradient convolution kernel compensation (fgCKC) decomposition algorithm for high-density surface EMG decomposition and apply it to an offline and real-time estimation of MU spike trains. We modified the calculation of the cross-correlation vectors to improve the calculation efficiency of the gradient convolution kernel compensation (gCKC) algorithm. Specifically, the new fgCKC algorithm considers the past gradient in addition to the current gradient. Furthermore, the EMG signals are divided by sliding windows to simulate real-time decomposition, and the proposed algorithm was validated on simulated and experimental signals. In the offline decomposition, fgCKC has the same robustness as gCKC, with sensitivity differences of 2.6 ± 1.3 % averaged across all trials and subjects. Nevertheless, depending on the number of MUs and the signal-to-noise ratio of signals, fgCKC is approximately 3 times faster than gCKC. In the real-time part, the processing only needed 240 ms average per window of EMG signals on a regular personal computer (IIntel(R) Core(TM) i5-12490F 3 GHz, 16 GB memory). These results indicate that fgCKC achieves real-time decomposition by significantly reducing processing time, providing more possibilities for non-invasive neuronal behavior research.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
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.
期刊最新文献
Inter-session Reliability of Magnetic Nerve Stimulation and Within-Session comparison to Electrical Nerve Stimulation in Evaluating Neuromuscular Function of Knee Extensor Muscles. Effect of tasks on intramuscular regional differences in rectus femoris elasticity during isometric contraction: An ultrasound shear wave elastography study. Flexor hallucis longus and tibialis anterior: A synergistic relationship. The unprecedented progresses in neuromechanics over the past 50 years - In celebration of the 50th anniversary of the international society of biomechanics. Wavelet-based time-frequency intermuscular beta-band coherence decreases with age but increases after mental fatigue in ankle muscles during gait independent of age.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1