Bispectral analysis of surface EMG

Kaplanis, P. A.; Pattichis, Constantinos S.; Hadjileontiadis, Leontios J.; Panas, S.M.

doi:10.1109/melcon.2000.880047

Cited by 30 publications

(41 citation statements)

References 9 publications

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“…Gaussianity test using db45 shows that the Gaussianity increased from trial 1 (slow walking) to trial 3 (fast walking) indicating decrease of muscle contraction during the trial (Fig.5). The achieved results support the outcome of Kaplanis et al [3], who have also used Bispectrum analysis for analyzing the biceps brachii muscle, where SEMG becomes less Gaussian with increased muscle contraction. 5.…”

Section: Gaussianity and Linearitysupporting

confidence: 85%

“…The raw EMG signal depicts the increment in the Interference Pattern (IP) [2]. The firing pulses are normally considered a random function of time, which is non-Gaussian in nature [3]. Quantitative analysis of the IP is useful in the diagnosis of neuromuscular disorders.…”

Section: Introductionmentioning

confidence: 99%

“…Bispectrum analysis, a particular form of higher-order spectra (HOS), was used by Kaplanis and Pattichis [3] for analyzing the "Biceps Brachii" muscle. The analysis revealed that SEMG turn into less Gaussian on escalating mean voluntary contraction (MVC).…”

Section: Introductionmentioning

confidence: 99%

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Effectiveness of the Wavelet Transform on the Surface EMG to Understand the Muscle Fatigue During Walk

Hussain¹,

Mamun²

2012

Measurement Science Review

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Muscle fatigue is the decline in ability of a muscle to create force. Electromyography (EMG) is a medical technique for measuring muscle response to nervous stimulation. During a sustained muscle contraction, the power spectrum of the EMG shifts towards lower frequencies. These effects are due to muscle fatigue. Muscle fatigue is often a result of unhealthy work practice. In this research, the effectiveness of the wavelet transform applied to the surface EMG (SEMG) signal as a means of understanding muscle fatigue during walk is presented. Power spectrum and bispectrum analysis on the EMG signal getting from right rectus femoris muscle is executed utilizing various wavelet functions (WFs). It is possible to recognize muscle fatigue appreciably with the proper choice of the WF. The outcome proves that the most momentous changes in the EMG power spectrum are symbolized by WF Daubechies45. Moreover, this research has compared bispectrum properties to the other WFs. To determine muscle fatigue during gait, Daubechies45 is used in this research to analyze the SEMG signal.

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Section: Gaussianity and Linearitysupporting

confidence: 85%

Section: Introductionmentioning

confidence: 99%

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Effectiveness of the Wavelet Transform on the Surface EMG to Understand the Muscle Fatigue During Walk

Hussain¹,

Mamun²

2012

Measurement Science Review

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“…For that purpose should apply phase unwrapping technique [6].For this purpose can need to relate integer, true and estimated biphase as below [7]. For finding of matrix n(k,l) can use a technique [3] and computed as…”

Section: B Cepstrum Of Bispectrum Recovery Techniquementioning

confidence: 99%

“…Mostly for detecting of EMG signals we are using surface electrodes because this process is very easy for detecting EMG signals. In mathematically the EMG signal [1] - [3]can be modeled as shown in equation.…”

Section: Introductionmentioning

confidence: 99%

Cepstrum of Bispectrum for MUAP Estimation from EMG Signals

Karthik¹

2017

IJRASET

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EMG signal gives the information regarding the working of nerves and the muscles. The shape of the MUAP is same until there is a movement in the position of electrode. In mathematically the EMG signal is modeled as the output signal of a filtered impulse process where the neuron firing impulses are assumed to be the input of a system whose transfer function is the motor unit action potential. For obtaining the motor unit action potentials we have to apply some higher order statics based reconstruction algorithm methods. this paper we report the results of applying cepstrum of bispectrum method to different type of EMG signals to characterize the motor unit action potentials in the muscle. In this paper we clearly obtain the shape of MUAP is same for different EMG signals. The characteristics of MUAP is different for the disease having neuropathy and myopathy. The MUAP for these type of diseases we can report in the results.

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Evaluation of muscle force classification using shape analysis of the sEMG probability density function: a simulation study

Ayachi

Boudaoud

Marque

2014

Med Biol Eng Comput

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In this work, we propose to classify, by simulation, the shape variability (or non-Gaussianity) of the surface electromyogram (sEMG) amplitude probability density function (PDF), according to contraction level, using high-order statistics (HOS) and a recent functional formalism, the core shape modeling (CSM). According to recent studies, based on simulated and/or experimental conditions, the sEMG PDF shape seems to be modified by many factors as: contraction level, fatigue state, muscle anatomy, used instrumentation, and also motor control parameters. For sensitivity evaluation against these several sources (physiological, instrumental, and neural control) of variability, a large-scale simulation (25 muscle anatomies, ten parameter configurations, three electrode arrangements) is performed, by using a recent sEMG-force model and parallel computing, to classify sEMG data from three contraction levels (20, 50, and 80% MVC). A shape clustering algorithm is then launched using five combinations of HOS parameters, the CSM method and compared to amplitude clustering with classical indicators [average rectified value (ARV) and root mean square (RMS)]. From the results screening, it appears that the CSM method obtains, using Laplacian electrode arrangement, the highest classification scores, after ARV and RMS approaches, and followed by one HOS combination. However, when some critical confounding parameters are changed, these scores decrease. These simulation results demonstrate that the shape screening of the sEMG amplitude PDF is a complex task which needs both efficient shape analysis methods and specific signal recording protocol to be properly used for tracking neural drive and muscle activation strategies with varying force contraction in complement to classical amplitude estimators.

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Bispectral analysis of surface EMG

Cited by 30 publications

References 9 publications

Effectiveness of the Wavelet Transform on the Surface EMG to Understand the Muscle Fatigue During Walk

Effectiveness of the Wavelet Transform on the Surface EMG to Understand the Muscle Fatigue During Walk

Cepstrum of Bispectrum for MUAP Estimation from EMG Signals

Evaluation of muscle force classification using shape analysis of the sEMG probability density function: a simulation study

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