Hurst's rescaled-range (R/S) analysis and fractal dimension of electromyographic (EMG) signal

Bodruzzaman, M.; Cadzow, James A.; Shiavi, Richard; Kilroy, A.; Dawant, Benoît B.M.; Wilkes, Mitch

doi:10.1109/secon.1991.147939

Cited by 14 publications

(4 citation statements)

References 5 publications

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“…Chaos is a complex motion which is always restricted in limited areas, extremely sensitive to initial values, long-term unpredictable, with track never repeated, fractal dimension and strange attractors. Amount of study demonstrated that Center Nervous System (CNS) generates chaotic firing patterns of action potentials; and a variety of physiological potential nerve signals, including EEG, ECG and EMG, etc., originated from CNS, have shown some degree of chaotic behavior, too [16][17][18][19][20][21][22][23][24][25][26][27][28]. Some nonlinear analysis methods are applied to the analysis of EMG.…”

Section: Chaotic Theory Analysis Methodsmentioning

confidence: 99%

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Application of the Chaos Theory in the Analysis of EMG on Patients with Facial Paralysis

Xiong

Zhao

Han

et al. 2014

Robot Intelligence Technology and Applications 2

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Abstract. Surface electromyography (sEMG) has been widely applied to disease diagnosis, pathologic analysis and rehabilitation evaluation. It is the nonlinear summation of the electrical activity of the motor units in a muscle and can reflect the state of neuromuscular function. Traditional linear and statistical analysis methods have some significant limitations due to the short-term stationary and lower signal-noise ratio of sEMG. In this paper, we introduce chaotic analysis into the field of sEMG process to investigate the hidden nonlinear characteristics of sEMG of patients with facial paralysis. sEMG on the bilateral masseter, levator labii superioris and frontalis of the 21 patients is recorded. Chaotic analysis is employed to extract new features, including correlation dimension, Lyapunov exponent, approximate entropy and so on. We discover the maximum Lyapunov exponents are all greater than 0, indicating that sEMG is a chaotic signal; correlation dimensions of sEMG on healthy sides are all smaller than that of diseased sides; and inversely, the approximate entropies of healthy sides are all greater than that of diseased sides. Consequently, chaotic analysis can provide a new insight into the complexity of the EMG and may be a vital indicator of diagnosis and recovery assessment of facial paralysis.

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Section: Chaotic Theory Analysis Methodsmentioning

confidence: 99%

“…Bodruzzaman et al extracted fractal dimension [18], Hurst's rescaledrange, Housdorff-Besicovich fractal dimension [19] and correlation dimension [20] respectively, to analyze sEMG acquired from the patients with neuromuscular diseases. The results indicated that chaotic features of sEMG are effective indicators for diseases diagnosis.…”

Section: Introductionmentioning

confidence: 99%

Application of the Chaos Theory in the Analysis of EMG on Patients with Facial Paralysis

Xiong

Zhao

Han

et al. 2014

Robot Intelligence Technology and Applications 2

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“…We, for example, note that the Hurst exponent obtained can be used to determine the fractal dimension [3], which is a typical measure of the roughness of the system in multifractal time-series. Figure 6 also indicates that a stretched exponential distribution [8] could be a good fit.…”

Section: Heteroskedacity and Fat Tailsmentioning

confidence: 99%

A statistical approach to risk mitigation in computational markets

Sandholm

Lai

2007

Proceedings of the 16th International Symposium on High Performance Distributed Computing

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We study stochastic models to mitigate the risk of poor Quality-of-Service (QoS) in computational markets. Consumers who purchase services expect both price and performance guarantees. They need to predict future demand to budget for sustained performance despite price fluctuations. Conversely, providers need to estimate demand to price future usage. The skewed and bursty nature of demand in large-scale computer networks challenges the common statistical assumptions of symmetry, independence, and stationarity. This discrepancy leads to underestimation of investment risk. We confirm this non-normal distribution behavior in our study of demand in computational markets.The high agility of a dynamic resource market requires flexible, efficient, and adaptable predictions. Computational needs are typically expressed using performance levels, hence we estimate worst-case bounds of price distributions to mitigate the risk of missing execution deadlines.To meet these needs, we use moving time windows of statistics to approximate price percentile functions. We use snapshots of summary statistics to calculate prediction intervals and estimate model uncertainty. Our approach is model-agnostic, distribution-free both in prices and prediction errors, and does not require extensive sampling nor manual parameter tuning. Our simulations and experiments show that a Chebyshev inequality model generates accurate predictions with minimal sample data requirements.We also show that this approach mitigates the risk of dropped service level performance when selecting hosts to run a bag-of-task Grid application simulation in a live computational market cluster.

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“…Kaedah ini digunakan untuk menentukan ciri fraktal siri masa dan dikenali sebagai analisis penskalaan julat (analisis vs). Mengikut analisis ini, isyarat dinamik s(t), boleh dicirikan gleh eksponen Hurst H, (Bodruzzaman 1991). Dimensi fraktal, Drdiberikan sebagai,…”

Section: Uktjran Kekalijtan: Dimensi Fraktal Dan Analisis R/sunclassified

Mencari Kewujudan Gelagat Kekalutan Siri Masa: Satu Analisis Awal terhadap Gelagat Kadar Faedah antara Bank

Yusof

Sembok²,

Noor

1999

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Hurst's rescaled-range (R/S) analysis and fractal dimension of electromyographic (EMG) signal

Cited by 14 publications

References 5 publications

Application of the Chaos Theory in the Analysis of EMG on Patients with Facial Paralysis

Application of the Chaos Theory in the Analysis of EMG on Patients with Facial Paralysis

A statistical approach to risk mitigation in computational markets

Mencari Kewujudan Gelagat Kekalutan Siri Masa: Satu Analisis Awal terhadap Gelagat Kadar Faedah antara Bank

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