On the use of high-order cumulant and bispectrum for muscular-activity detection

Orosco, Eugenio; Díez, Pablo; Laciar, Eric; Mut, Vicente; Soria, Carlos; Sciascio, Fernando di

doi:10.1016/j.bspc.2015.02.011

Cited by 22 publications

(9 citation statements)

References 55 publications

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“…Further, accurate modeling of the stochastic properties of EMG signals is important in extracting the features of such signals and quantitatively evaluating neuromuscular activity. Recent studies have suggested that the non-Gaussianity of EMG signals serves as a potentially important index for EMG pattern classification [29], muscular-activity detection [30], and evaluation of neuromuscular disease [31]. Accordingly, higher performance may be realized in application fields such as prosthesis control if novel EMG processing techniques can be developed using the scale mixture model.…”

Section: Discussionmentioning

confidence: 99%

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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Section: Discussionmentioning

confidence: 99%

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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“…Such functions allow the description of the statistical properties of non-Gaussian processes with reasonable accuracy [10,14]. The HOS techniques are used for the development of the signal detection methods [15,22]. However, these methods have some restrictions, for instance the detection of deterministic signals, and the imposition that only thirdorder statistics can be used.…”

Section: Introductionmentioning

confidence: 99%

Signal Detection in Correlated Non-Gaussian Noise Using Higher-Order Statistics

Palahina

Gamcová

Gladisova

et al. 2017

Circuits Syst Signal Process

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The authors of this paper study the synthesis of new models and methods for signal detection in additive correlated non-Gaussian noise. A new moment quality criterion decision making is proposed based on a random process description using moments and a formation of polynomial decision rules. Taking into account parameters of non-Gaussian distribution of random variables (such as the moments of third and higher orders and joint cumulants), it is shown that nonlinear processing of samples can increase the signal processing efficiency. A synthesis of effective methods and algorithms of data processing in non-Gaussian noise is also presented in this work.

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“…Raghuvee and Nikias presented two parametric approaches consisting of the third-order recursion method and the constrained third-order mean method to estimate the bispectrum [30,31]. Bispectrum has been widely used in many fields, such as electrical and mechanical equipment condition monitoring and diagnostics [32][33][34], astronomy [35], communication [36,37] and biomedical engineering [38,39].…”

Section: Introductionmentioning

confidence: 99%

Series Arc Fault Detection Algorithm Based on Autoregressive Bispectrum Analysis

et al. 2015

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Arc fault is one of the most critical reasons for electrical fires. Due to the diversity, randomness and concealment of arc faults in low-voltage circuits, it is difficult for general methods to protect all loads from series arc faults. From the analysis of many series arc faults, a large number of high frequency signals generated in circuits are found. These signals are easily affected by Gaussian noise which is difficult to be eliminated as a result of frequency aliasing. Thus, a novel detection algorithm is developed to accurately detect series arc faults in this paper. Initially, an autoregressive model of the mixed high frequency signals is modelled. Then, autoregressive bispectrum analysis is introduced to analyze common series arc fault features. The phase information of arc fault signal is preserved using this method. The influence of Gaussian noise is restrained effectively. Afterwards, several features including characteristic frequency, fluctuation of phase angles, diffused distribution and incremental numbers of bispectrum peaks are extracted for recognizing arc faults. Finally, least squares support vector machine is used to accurately identify series arc faults from the load states based on these frequency features of bispectrum. The validity of the algorithm is experimentally verified obtaining arc fault detection rate above 97%. OPEN ACCESSAlgorithms 2015, 8 930

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On the use of high-order cumulant and bispectrum for muscular-activity detection

Cited by 22 publications

References 55 publications

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

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

Signal Detection in Correlated Non-Gaussian Noise Using Higher-Order Statistics

Series Arc Fault Detection Algorithm Based on Autoregressive Bispectrum Analysis

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