A simulation study on the relation between muscle motor unit numbers and the non-Gaussianity/non-linearity levels of surface electromyography

Yan, Zhao; Li, Dongxu

doi:10.1007/s11427-012-4400-1

Cited by 9 publications

(5 citation statements)

References 26 publications

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“…In this study, the Gaussianity test developed by Hinich [22] which tests the nonskewness of a time series has been used. This is because a relationship between number of active motor units and these features has been established [8]. …”

Section: Methodsmentioning

confidence: 99%

“…Zhao and Li [8] used a sEMG model of the biceps brachii and determined the effect of the number of active motor units and firing rate schemes on the higher-order statistics of the signal: Gaussianity and linearity. It was shown that the number of active motor units is correlated with Gaussianity of the sEMG signal [8] and it was demonstrated that the decreased number of motor units due to motor unit remodelling can be studied with Gaussianity.…”

Section: Introductionmentioning

confidence: 99%

“…It was shown that the number of active motor units is correlated with Gaussianity of the sEMG signal [8] and it was demonstrated that the decreased number of motor units due to motor unit remodelling can be studied with Gaussianity. This concept has been experimentally validated for the m. biceps brachii , showing increasing Gaussianity of the sEMG with increasing force [9–11].…”

Section: Introductionmentioning

confidence: 99%

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Age-Associated Changes in the Spectral and Statistical Parameters of Surface Electromyogram ofTibialis Anterior

Siddiqi

Arjunan

Kumar

2016

BioMed Research International

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Age-related neuromuscular change of Tibialis Anterior (TA) is a leading cause of muscle strength decline among the elderly. This study has established the baseline for age-associated changes in sEMG of TA at different levels of voluntary contraction. We have investigated the use of Gaussianity and maximal power of the power spectral density (PSD) as suitable features to identify age-associated changes in the surface electromyogram (sEMG). Eighteen younger (20–30 years) and 18 older (60–85 years) cohorts completed two trials of isometric dorsiflexion at four different force levels between 10% and 50% of the maximal voluntary contraction. Gaussianity and maximal power of the PSD of sEMG were determined. Results show a significant increase in sEMG's maximal power of the PSD and Gaussianity with increase in force for both cohorts. It was also observed that older cohorts had higher maximal power of the PSD and lower Gaussianity. These age-related differences observed in the PSD and Gaussianity could be due to motor unit remodelling. This can be useful for noninvasive tracking of age-associated neuromuscular changes.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Age-Associated Changes in the Spectral and Statistical Parameters of Surface Electromyogram ofTibialis Anterior

Siddiqi

Arjunan

Kumar

2016

BioMed Research International

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“…This approach revealed that EMG signals have a heavier-tailed distribution than in the Gaussian case at low contraction levels [20], [21]. Some simulation studies have also reported that the non-Gaussianity of EMG signals varies depending on the muscle contraction level such that the increase in the contraction level shifts the probability distribution of EMG signals towards the Gaussian distribution [22], [23]. Despite these experimental reports, there is no general consensus on the distribution of EMG signals, and a stochastic model that can represent the non-Gaussianity of EMG signals depending on muscle activity has not been developed.…”

Section: Introductionmentioning

confidence: 94%

A Scale Mixture-Based Stochastic Model of Surface EMG Signals With Variable Variances

Furui

Hayashi

Tsuji

2019

IEEE Trans. Biomed. Eng.

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Objective: Surface electromyogram (EMG) signals have typically been assumed to follow a Gaussian distribution. However, the presence of non-Gaussian signals associated with muscle activity has been reported in recent studies, and there is no general model of the distribution of EMG signals that can explain both non-Gaussian and Gaussian distributions within a unified scheme. Methods: In this paper, we describe the formulation of a non-Gaussian EMG model based on a scale mixture distribution. In the model, an EMG signal at a certain time follows a Gaussian distribution, and its variance is handled as a random variable that follows an inverse gamma distribution. Accordingly, the probability distribution of EMG signals is assumed to be a mixture of Gaussians with the same mean but different variances. The EMG variance distribution is estimated via marginal likelihood maximization. Results: Experiments involving nine participants revealed that the proposed model provides a better fit to recorded EMG signals than conventional EMG models. It was also shown that variance distribution parameters may reflect underlying motor unit activity. Conclusion: This study proposed a scale mixture distribution-based stochastic EMG model capable of representing changes in non-Gaussianity associated with muscle activity. A series of experiments demonstrated the validity of the model and highlighted the relationship between the variance distribution and muscle force. Significance: The proposed model helps to clarify conventional wisdom regarding the probability distribution of surface EMG signals within a unified scheme.

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“…Activated by efferent neural drive, a motoneuron generates a series of MU action potentials (MUAPs), which propagate down to the neuromuscular junction (NMJ) and then transmit to the muscle fibers. The number of MUs [ 28 ], the function of NMJ propagation [ 29 ], and the innervation of muscle fibers [ 30 ] are all crucial factors that influence the formation of APs, the special and temporal summation of which are also referred to as electromyography (EMG) signals. It has been observed that during aging, there is preferential denervation of fast fibers with reinnervation via axonal sprouting from slow motor neurons, resulting in a conversion from type II (fast) fibers to type I (slow) fibers [ 31 ], and further changes in electrophysiological properties [ 32 ].…”

Section: Introductionmentioning

confidence: 99%

Exploration of a machine learning approach for diagnosing sarcopenia among Chinese community-dwelling older adults using sEMG-based data

Li,

Ou,

et al. 2024

J NeuroEngineering Rehabil

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Background In the practical application of sarcopenia screening, there is a need for faster, time-saving, and community-friendly detection methods. The primary purpose of this study was to perform sarcopenia screening in community-dwelling older adults and investigate whether surface electromyogram (sEMG) from hand grip could potentially be used to detect sarcopenia using machine learning (ML) methods with reasonable features extracted from sEMG signals. The secondary aim was to provide the interpretability of the obtained ML models using a novel feature importance estimation method. Methods A total of 158 community-dwelling older residents (≥ 60 years old) were recruited. After screening through the diagnostic criteria of the Asian Working Group for Sarcopenia in 2019 (AWGS 2019) and data quality check, participants were assigned to the healthy group (n = 45) and the sarcopenic group (n = 48). sEMG signals from six forearm muscles were recorded during the hand grip task at 20% maximal voluntary contraction (MVC) and 50% MVC. After filtering recorded signals, nine representative features were extracted, including six time-domain features plus three time-frequency domain features. Then, a voting classifier ensembled by a support vector machine (SVM), a random forest (RF), and a gradient boosting machine (GBM) was implemented to classify healthy versus sarcopenic participants. Finally, the SHapley Additive exPlanations (SHAP) method was utilized to investigate feature importance during classification. Results Seven out of the nine features exhibited statistically significant differences between healthy and sarcopenic participants in both 20% and 50% MVC tests. Using these features, the voting classifier achieved 80% sensitivity and 73% accuracy through a five-fold cross-validation. Such performance was better than each of the SVM, RF, and GBM models alone. Lastly, SHAP results revealed that the wavelength (WL) and the kurtosis of continuous wavelet transform coefficients (CWT_kurtosis) had the highest feature impact scores. Conclusion This study proposed a method for community-based sarcopenia screening using sEMG signals of forearm muscles. Using a voting classifier with nine representative features, the accuracy exceeds 70% and the sensitivity exceeds 75%, indicating moderate classification performance. Interpretable results obtained from the SHAP model suggest that motor unit (MU) activation mode may be a key factor affecting sarcopenia.

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A simulation study on the relation between muscle motor unit numbers and the non-Gaussianity/non-linearity levels of surface electromyography

Cited by 9 publications

References 26 publications

Age-Associated Changes in the Spectral and Statistical Parameters of Surface Electromyogram ofTibialis Anterior

Age-Associated Changes in the Spectral and Statistical Parameters of Surface Electromyogram ofTibialis Anterior

A Scale Mixture-Based Stochastic Model of Surface EMG Signals With Variable Variances

Exploration of a machine learning approach for diagnosing sarcopenia among Chinese community-dwelling older adults using sEMG-based data

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