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Muscle Fatigue Analysis With Optimized Complementary Ensemble Empirical Mode Decomposition and Multi-Scale Envelope Spectral Entropy

Zhao, Juan and She, Jinhua and Fukushima, Edwardo F. and Wang, Dianhong and Wu, Min and Pan, Katherine (2020) Muscle Fatigue Analysis With Optimized Complementary Ensemble Empirical Mode Decomposition and Multi-Scale Envelope Spectral Entropy. Frontiers in Neurorobotics, 14 . Art. No. 566172. ISSN 1662-5218. PMCID PMC7674835. https://resolver.caltech.edu/CaltechAUTHORS:20201208-163034197

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Abstract

The preprocessing of surface electromyography (sEMG) signals with complementary ensemble empirical mode decomposition (CEEMD) improves frequency identification precision and temporal resolution, and lays a good foundation for feature extraction. However, a mode-mixing problem often occurs when the CEEMD decomposes an sEMG signal that exhibits intermittency and contains components with a near-by spectrum into intrinsic mode functions (IMFs). This paper presents a method called optimized CEEMD (OCEEMD) to solve this problem. The method integrates the least-squares mutual information (LSMI) and the chaotic quantum particle swarm optimization (CQPSO) algorithm in signal decomposition. It uses the LSMI to calculate the correlation between IMFs so as to reduce mode mixing and uses the CQPSO to optimize the standard deviation of Gaussian white noise so as to improve iteration efficiency. Then, useful IMFs are selected and added to reconstruct a de-noised signal. Finally, considering that the IMFs contain abundant frequency and envelope information, this paper extracts the multi-scale envelope spectral entropy (MSESEn) from the reconstructed sEMG signal. Some original sEMG signals, which were collected from experiments, were used to validate the methods. Compared with the CEEMD and complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), the OCEEMD effectively suppresses mode mixing between IMFs with rapid iteration. Compared with approximate entropy (ApEn) and sample entropy (SampEn), the MSESEn clearly shows a declining tendency with time and is sensitive to muscle fatigue. This suggests a potential use of this approach for sEMG signal preprocessing and the analysis of muscle fatigue.


Item Type:Article
Related URLs:
URLURL TypeDescription
https://doi.org/10.3389/fnbot.2020.566172DOIArticle
http://www.ncbi.nlm.nih.gov/pmc/articles/pmc7674835/PubMed CentralArticle
Additional Information:© 2020 Zhao, She, Fukushima, Wang, Wu and Pan. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. Received: 27 May 2020; Accepted: 18 September 2020; Published: 05 November 2020. The authors would like to thank the research team at the Advanced Mechatronics Laboratory, School of Engineering, Tokyo University of Technology, Japan, and Mr. Ruoyu Jiang, Mr. Yujian Zhou, Mr. Wangyang Ge, Mr. Qicheng Mei, and Mr. Zewen Wang at School of Automation, China University of Geosciences, Wuhan, China, for their contributions to this project. This work was supported in part by the National Key Research and Development Program of China under Grant 2017YFB1300900; the National Natural Science Foundation of China under Grant 61873348; the Natural Science Foundation of Hubei Province, China, under Grant 2020CFA031; Wuhan Applied Foundational Frontier Project under Grant 2020010601012175; the 111 Project, China, under Grant B17040; and JSPS KAKENHI, Japan, under Grant 20H04566. Author Contributions. JZ and JS conceived this study. JZ and KP performed the experiments and wrote the manuscript. JS, EF, DW, and MW contributed to the methodology. JZ, JS, and KP revised the manuscript. All the co-authors agreed with the present version. Data Availability Statement. The raw data were recorded at the Advanced Mechatronics Laboratory, School of Engineering, Tokyo University of Technology, Japan. The data supporting the findings of this study are available from zhaojuan0859@cug.edu.cn or she@stf.teu.ac.jp on request. Ethics Statement. The studies involving human participants were reviewed and approved by The ethical committee of Tokyo University of Technology. The patients/participants provided their written informed consent to participate in this study. The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Funders:
Funding AgencyGrant Number
National Key Research and Development Program of China2017YFB1300900
National Natural Science Foundation of China61873348
National Science Foundation of Hubei Province2020CFA031
Wuhan Applied Foundational Frontier Project2020010601012175
111 Project of ChinaB17040
Japan Society for the Promotion of Science (JSPS)20H04566
Subject Keywords:surface electromyography, complementary ensemble empirical mode decomposition, least-squares mutual information, multi-scale envelope spectral entropy, muscle fatigue
PubMed Central ID:PMC7674835
Record Number:CaltechAUTHORS:20201208-163034197
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20201208-163034197
Official Citation:Zhao J, She J, Fukushima EF, Wang D, Wu M and Pan K (2020) Muscle Fatigue Analysis With Optimized Complementary Ensemble Empirical Mode Decomposition and Multi-Scale Envelope Spectral Entropy. Front. Neurorobot. 14:566172. doi: 10.3389/fnbot.2020.566172
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:106972
Collection:CaltechAUTHORS
Deposited By: George Porter
Deposited On:09 Dec 2020 15:26
Last Modified:09 Dec 2020 15:26

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