Fatiguing Effects on the Multi-Scale Entropy of Surface Electromyography in Children with Cerebral Palsy

Hong, Tong Zheng; Zhang, Xu; Ma, Hongjun; Chen, Yan; Chen, Xiang

doi:10.3390/e18050177

Cited by 16 publications

(29 citation statements)

References 51 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The coefficient curves can further reveal the abnormal MU activation patterns during crawling in CP. Some researchers have found that motor dysfunction of CP manifested as abnormal entropy of multi-scale oscillations of sEMG activities in CP, and a major peripheral cause of these abnormalities is abnormal MU recruitment ( 10 , 16 ). This study revealed that motor dysfunction of CP also manifested as abnormal recruitment of oscillation synergies.…”

Section: Discussionmentioning

confidence: 99%

“…Gross et al ( 8 ) found that co-activation index of the rectus femoris/semitendinosus couple was more sensitive to speed, which could be explained by altered motor control. Furthermore, increasing studies suggested that the motor dysfunction caused by CP affected not only the overall sEMG activity but also the sEMG frequency components or oscillations ( 9 , 10 ).…”

Section: Introductionmentioning

confidence: 99%

“…In addition, studies also reported that coherence can be affected by voluntary force ( 15 ). More recent studies employed sEMG oscillation components for abnormal motor functions analysis, and found that the characteristics of sEMG oscillation components correspond to various neuromuscular damages in CP ( 10 , 16 ). On other hand, sEMG frequency spectrum is correlated the types of recruited MUs, Wakeling et al have demonstrated that slower and faster MUs in muscles indeed generate low and high sEMG frequency components, respectively ( 17 , 18 ).…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Degraded Synergistic Recruitment of sEMG Oscillations for Cerebral Palsy Infants Crawling

et al. 2018

View full text Add to dashboard Cite

Background: Synergistic recruitment of muscular activities is a generally accepted mechanism for motor function control, and motor dysfunction, such as cerebral palsy (CP), destroyed the synergistic electromyography activities of muscle group for limb movement. However, very little is known how motor dysfunction of CP affects the organization of the myoelectric frequency components due to the abnormal motor unit recruiting patterns.Objectives: Exploring whether the myoelectric activity can be represented with synergistic recruitment of surface electromyography (sEMG) frequency components; evaluating the effect of CP motor dysfunction on the synergistic recruitment of sEMG oscillations.Methods: Twelve CP infants and 17 typically developed (TD) infants are recruited for self-paced crawling on hands and knees. sEMG signals have been recorded from bilateral biceps brachii (BB) and triceps brachii (TB) muscles. Multi-scale oscillations are extracted via multivariate empirical mode decomposition (MEMD), and non-negative matrix factorization (NMF) method is employed to obtain synergistic pattern of these sEMG oscillations. The coefficient curve of sEMG oscillation synergies are adopted to quantify the time-varying recruitment of BB and TB myoelectric activity during infants crawling.Results: Three patterns of sEMG oscillation synergies with specific frequency ranges are extracted in BB and TB of CP or TD infants. The contribution of low-frequency oscillation synergy of BB in CP group is significantly less than that in TD group (p < 0.05) during forward swing phase for slow contraction; however, this low-frequency oscillation synergy keep higher level during the backward swing phase crawling. For the myoelectric activities of TB, there is not enough high-frequency oscillation recruitment of sEMG for the fast contraction in propulsive phase of CP infants crawling.Conclusion: Our results reveal that, the myoelectric activities of a muscle can be manifested as sEMG oscillation synergies, and motor dysfunction of CP degrade the synergistic recruitment of sEMG oscillations due to the impaired CNS regulation and destroyed MU/muscle fiber. Our preliminary work suggests that time-varying coefficient curve of sEMG oscillation synergies is a potential index to evaluate the abnormal recruitment of electromyography activities affected by CP disorders.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Degraded Synergistic Recruitment of sEMG Oscillations for Cerebral Palsy Infants Crawling

et al. 2018

View full text Add to dashboard Cite

show abstract

“…MSE is used to calculate the sample entropy on multiple scales of the original signal and it is obtained by using a coarse granulation process at different scales [ 44 , 45 , 46 , 47 ].…”

Section: Methodsmentioning

confidence: 99%

Magnetotelluric Signal-Noise Identification and Separation Based on ApEn-MSE and StOMP

Cai

Peng

et al. 2019

Entropy

View full text Add to dashboard Cite

Natural magnetotelluric signals are extremely weak and susceptible to various types of noise pollution. To obtain more useful magnetotelluric data for further analysis and research, effective signal-noise identification and separation is critical. To this end, we propose a novel method of magnetotelluric signal-noise identification and separation based on ApEn-MSE and Stagewise orthogonal matching pursuit (StOMP). Parameters with good irregularity metrics are introduced: Approximate entropy (ApEn) and multiscale entropy (MSE), in combination with k-means clustering, can be used to accurately identify the data segments that are disturbed by noise. Stagewise orthogonal matching pursuit (StOMP) is used for noise suppression only in data segments identified as containing strong interference. Finally, we reconstructed the signal. The results show that the proposed method can better preserve the low-frequency slow-change information of the magnetotelluric signal compared with just using StOMP, thus avoiding the loss of useful information due to over-processing, while producing a smoother and more continuous apparent resistivity curve. Moreover, the results more accurately reflect the inherent electrical structure information of the measured site itself.

show abstract

“…The empirical mode decomposition (EMD) (Shang et al, 2011 ; Hong et al, 2016 ) was employed to extract multichannel-recorded sEMG features for pattern recognition. In each trial, a 4-second-recorded sEMG was extracted per channel, and the EMD was used to decompose the sEMG into eight intrinsic mode functions (IMFs) as

where r is the residual component, means the central tendency of sEMG signal.…”

Section: Methodsmentioning

confidence: 99%

Non-Uniform Sample Assignment in Training Set Improving Recognition of Hand Gestures Dominated with Similar Muscle Activities

Zhang

Liao²,

et al. 2018

Front. Neurorobot.

View full text Add to dashboard Cite

So far, little is known how the sample assignment of surface electromyogram (sEMG) features in training set influences the recognition efficiency of hand gesture, and the aim of this study is to explore the impact of different sample arrangements in training set on the classification of hand gestures dominated with similar muscle activation patterns. Seven right-handed healthy subjects (24.2 ± 1.2 years) were recruited to perform similar grasping tasks (fist, spherical, and cylindrical grasping) and similar pinch tasks (finger, key, and tape pinch). Each task was sustained for 4 s and followed by a 5-s rest interval to avoid fatigue, and the procedure was repeated 60 times for every task. sEMG were recorded from six forearm hand muscles during grasping or pinch tasks, and 4-s sEMG from each channel was segmented for empirical mode decomposition analysis trial by trial. The muscle activity was quantified with zero crossing (ZC) and Wilson amplitude (WAMP) of the first four resulting intrinsic mode function. Thereafter, a sEMG feature vector was constructed with the ZC and WAMP of each channel sEMG, and a classifier combined with support vector machine and genetic algorithm was used for hand gesture recognition. The sample number for each hand gesture was designed to be rearranged according to different sample proportion in training set, and corresponding recognition rate was calculated to evaluate the effect of sample assignment change on gesture classification. Either for similar grasping or pinch tasks, the sample assignment change in training set affected the overall recognition rate of candidate hand gesture. Compare to conventional results with uniformly assigned training samples, the recognition rate of similar pinch gestures was significantly improved when the sample of finger-, key-, and tape-pinch gesture were assigned as 60, 20, and 20%, respectively. Similarly, the recognition rate of similar grasping gestures also rose when the sample proportion of fist, spherical, and cylindrical grasping was 40, 30, and 30%, respectively. Our results suggested that the recognition rate of hand gestures can be regulated by change sample arrangement in training set, which can be potentially used to improve fine-gesture recognition for myoelectric robotic hand exoskeleton control.

show abstract

Fatiguing Effects on the Multi-Scale Entropy of Surface Electromyography in Children with Cerebral Palsy

Cited by 16 publications

References 51 publications

Degraded Synergistic Recruitment of sEMG Oscillations for Cerebral Palsy Infants Crawling

Degraded Synergistic Recruitment of sEMG Oscillations for Cerebral Palsy Infants Crawling

Magnetotelluric Signal-Noise Identification and Separation Based on ApEn-MSE and StOMP

Non-Uniform Sample Assignment in Training Set Improving Recognition of Hand Gestures Dominated with Similar Muscle Activities

Contact Info

Product

Resources

About