Nonlinear algorithm for surface electromyogram signals during lateral femoral muscle fatigue(PDF)
《中国医学物理学杂志》[ISSN:1005-202X/CN:44-1351/R]
- Issue:
- 2026年第1期
- Page:
- 90-98
- Research Field:
- 医学信号处理与医学仪器
- Publishing date:
Info
- Title:
- Nonlinear algorithm for surface electromyogram signals during lateral femoral muscle fatigue
- Author(s):
- CAO Chunxia1; QIAO Xiufang1; WANG Ruiyuan2; LI Junping2
- 1. School of Physical Education, China University of Mining and Technology, Xuzhou 221000, China 2. College of Sports and Human Sciences, Beijing Sport University, Beijing 100084, China
- Keywords:
- Keywords: skeletal muscle high-density array surface electromyography fatigue nonlinear algorithm
- PACS:
- R312；G804.21
- DOI:
- DOI:10.3969/j.issn.1005-202X.2026.01.012
- Abstract:
- Abstract: Objective To investigate the nonlinear dynamic characteristics of electromyogram (EMG) signals during skeletal muscle fatigue by analyzing EMG signals during the lateral femoral muscle fatigue. Methods Fifteen subjects underwent a maximal voluntary contraction (MVC) test on an isokinetic dynamometer, and they performed contractions at intensities of 30%, 50%, and 70% MVC until fatigue, with synchronous recording of high-density array surface EMG signals. Results (1) At graded exercise intensities, average rectified value (ARV) and root mean square (RMS)amplitude of EMG signals increased linearly with skeletal muscle fatigue (P<0.05), with the highest EMG ARV and RMS values at 70% MVC and the lowest EMG ARV and RMS values at 30% MVC (P<0.05). (2) EMG mean power frequency (MPF) value decreased linearly with fatigue (P<0.05). The absolute value of the MPF decline slope was the largest at 70% MVC, and the smallest at 30% MVC (P<0.05). The median frequency trended downward during 50% and 70% MVC muscles contraction to fatigue (P<0.05), while the median frequency firstly increased and then decreased during 30% MVC muscle contraction to fatigue (P<0.05). (3) Nonlinear indexes, including complexity (LZc), fractal dimension, multiscale sample entropy, and Kolmogorovs entropy all showed linear decreases with fatigue (P<0.05). The largest Lyapunov exponent value at 30% and 50% MVC increased with fatigue (P<0.05), whereas it displayed a trend of initial increase followed by decrease at 70% MVC (P<0.05). (4) The spatial domain index, motor variability, increased linearly with fatigue (P<0.05). Conclusion EMG signal is a kind of chaotic signal between periodic and random signals, possessing nonlinear characteristics. Therefore, nonlinear characteristics such as LZc, fractal dimension, and largest Lyapunov exponent can be used to assess the state of muscle fatigue.
Last Update: 2026-01-27