Relevant bibliographies by topics / Detrended fractal analysis

Academic literature on the topic 'Detrended fractal analysis'

Author: Grafiati
Published: 10 December 2022
Last updated: 28 January 2023

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Journal articles on the topic "Detrended fractal analysis"

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Qiu, Xiao Hong, Yong Bo Tan, and Bo Li. "Fractal Analysis of the Optimal Objective Function." Applied Mechanics and Materials 519-520 (February 2014): 811–15. http://dx.doi.org/10.4028/www.scientific.net/amm.519-520.811.

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The fractals of the optimization problems are first discussed. The multi-fractal parameters of the optimal objective function are computed by the Detrended Fluctuation Analysis (DFA) method. The multi-fractal general Hurst Index is related to the difficulty to solve the optimization problem. These features are verified by analyzing the first six test functions proposed on 2005 IEEE Congress on Evolutionary Computation. The results show that the different objective functions have obvious different multifractal and the general Hurst Index can be used to evaluate the difficulty to solve the optimization problem.
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Zhang, Xi, Xu Wu, Linlin Zhang, and Zhonglu Chen. "The Evaluation of Mean-Detrended Cross-Correlation Analysis Portfolio Strategy for Multiple risk Assets." Evaluation Review 46, no. 2 (February 3, 2022): 138–64. http://dx.doi.org/10.1177/0193841x221078642.

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The fractal characteristics of the security market were considered in portfolio strategy optimization. First, the detrended cross-correlation analysis was adopted to measure the fractal correlation of different securities. Second, the fractal correlation was embedded into the mean-variance criterion of the modern portfolio theory. Third, the mean-detrended cross-correlation analysis portfolio strategy of multiple risk assets was constructed, and the analytic solution of the strategy was given. Finally, the evaluation results revealed that the constructed the mean-detrended cross-correlation analysis portfolio strategy clearly improved investment performance, thus achieving the goal of optimizing the multiple risk asset portfolio strategy.
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Cabrera-Brito, Laura, German Rodriguez, Luis García-Weil, Mercedes Pacheco, Esther Perez, and Joanna J. Waniek. "Fractal Analysis of Deep Ocean Current Speed Time Series." Journal of Atmospheric and Oceanic Technology 34, no. 4 (April 2017): 817–27. http://dx.doi.org/10.1175/jtech-d-16-0098.1.

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AbstractFractal properties of deep ocean current speed time series, measured at a single-point mooring on the Madeira Abyssal Plain at 1000- and 3000-m depth, are explored over the range between one week and 5 years, by using the detrended fluctuation analysis and multifractal detrended fluctuation analysis methodologies. The detrended fluctuation analysis reveals the existence of two subranges with different scaling behaviors. Long-range temporal correlations following a power law are found in the time-scale range between approximately 50 days and 5 years, while a Brownian motion–type behavior is observed for shorter time scales. The multifractal analysis approach underlines a multifractal structure whose intensity decreases with depth. The analysis of the shuffled and surrogate versions of the original time series shows that multifractality is mainly due to long-range correlations, although there is a weak nonlinear contribution at 1000-m depth, which is confirmed by the detrended fluctuation analysis of volatility time series.
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ZHANG, JUNHUAN, and JUN WANG. "FRACTAL DETRENDED FLUCTUATION ANALYSIS OF CHINESE ENERGY MARKETS." International Journal of Bifurcation and Chaos 20, no. 11 (November 2010): 3753–68. http://dx.doi.org/10.1142/s0218127410028082.

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In this paper, we analyze and compare long-range power-law correlations of returns, absolute returns, squared returns, cubed returns and square waved returns for sixteen individual stocks from the block of energy sources of Chinese stock market and five stock indices (Shanghai Composite Index, Shenzhen Component Index, Dow Jones Industrial Average index, Nasdaq Composite Index, the Standard and Poor's 500 Index) by using a detrended fluctuation analysis approach. The empirical evidence suggests that Shanghai Composite Index is very close to Shenzhen Component Index and Nasdaq, DJIA is very close to S&P 500 in all cases. And the exponent trends of the returns are close to that of square waved returns. Also, five indices deviate from other sixteen individual energy stocks in all cases except square waved returns. Further, there are long-range correlations and persistence in volatility series of absolute returns and squared returns. Moreover, we investigate the long-term memory of these returns by applying Lo's modified rescaled range statistic. We find that the China energy market exhibits fractal and persistence properties.
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KONG, QINGGE, QING YU, MEIFENG DAI, YUE ZONG, XIAODONG WANG, JIANZHONG JIANG, and HUOJUN RUAN. "MULTIFRACTAL DETRENDED FLUCTUATION ANALYSIS BASED ON PSEUDO-BILINEAR FRACTAL INTERPOLATION FUNCTIONS ON METALLIC GLASSES." Fractals 26, no. 04 (August 2018): 1850047. http://dx.doi.org/10.1142/s0218348x18500470.

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Based on the multifractal detrended cross-correlation analysis, which is the most effective way to detect long-range cross-correlation of time series, in this paper, we present a new method called multifractal detrended fluctuation analysis based on pseudo-bilinear fractal interpolation functions (MFDFA-PBFIF). In order to get a better detrended effect, we replace the polynomial fitting with PBFIFs in detrended process, and the result shows that the MFDFA-PBFIF can achieve a more accurate result. Then, we analyze the Legendre spectrum to detect the multifractal property on metallic glasses with MFDFA-PBFIF.
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Zhang, Daoming, Cong Wang, Chuangye Li, and Wei Dai. "Multi-fractal detrended fluctuation half-spectrum analysis of HRV." Journal of Engineering 2019, no. 22 (November 1, 2019): 8315–18. http://dx.doi.org/10.1049/joe.2019.1067.

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Tu, Tongbi, Ali Ercan, and M. Levent Kavvas. "Fractal scaling analysis of groundwater dynamics in confined aquifers." Earth System Dynamics 8, no. 4 (October 24, 2017): 931–49. http://dx.doi.org/10.5194/esd-8-931-2017.

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Abstract. Groundwater closely interacts with surface water and even climate systems in most hydroclimatic settings. Fractal scaling analysis of groundwater dynamics is of significance in modeling hydrological processes by considering potential temporal long-range dependence and scaling crossovers in the groundwater level fluctuations. In this study, it is demonstrated that the groundwater level fluctuations in confined aquifer wells with long observations exhibit site-specific fractal scaling behavior. Detrended fluctuation analysis (DFA) was utilized to quantify the monofractality, and multifractal detrended fluctuation analysis (MF-DFA) and multiscale multifractal analysis (MMA) were employed to examine the multifractal behavior. The DFA results indicated that fractals exist in groundwater level time series, and it was shown that the estimated Hurst exponent is closely dependent on the length and specific time interval of the time series. The MF-DFA and MMA analyses showed that different levels of multifractality exist, which may be partially due to a broad probability density distribution with infinite moments. Furthermore, it is demonstrated that the underlying distribution of groundwater level fluctuations exhibits either non-Gaussian characteristics, which may be fitted by the Lévy stable distribution, or Gaussian characteristics depending on the site characteristics. However, fractional Brownian motion (fBm), which has been identified as an appropriate model to characterize groundwater level fluctuation, is Gaussian with finite moments. Therefore, fBm may be inadequate for the description of physical processes with infinite moments, such as the groundwater level fluctuations in this study. It is concluded that there is a need for generalized governing equations of groundwater flow processes that can model both the long-memory behavior and the Brownian finite-memory behavior.
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Wan, Li, Peng Chen, and Zhao Xian Gong. "Quantifying Fractal Dynamics of Metallogenic Systems with Detrended Fluctuation Analysis." Applied Mechanics and Materials 249-250 (December 2012): 26–30. http://dx.doi.org/10.4028/www.scientific.net/amm.249-250.26.

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In this paper, we analysed fractional dynamics behavior in metallogenic elements grade series, using detrended fluctuation analysis (DFA), with the objective to explore and understand the underlying dynamic mechanism. Our results show that the metallogenic elements grade series are the scale invariance and the long-range correlation. As in the case of element grade dynamics, the DFA scaling exponents can be used to discriminate mineral intensity.
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Li, Zhongwei, and You-Kuan Zhang. "Quantifying fractal dynamics of groundwater systems with detrended fluctuation analysis." Journal of Hydrology 336, no. 1-2 (March 2007): 139–46. http://dx.doi.org/10.1016/j.jhydrol.2006.12.017.

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Arsac, Laurent M., and Véronique Deschodt-Arsac. "Detrended fluctuation analysis in a simple spreadsheet as a tool for teaching fractal physiology." Advances in Physiology Education 42, no. 3 (September 1, 2018): 493–99. http://dx.doi.org/10.1152/advan.00181.2017.

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Fractal physiology demonstrated growing interest over the last decades among physiologists, neuroscientists, and clinicians. Many physiological systems coordinate themselves for reducing variability and maintain a steady state. When recorded over time, the output signal exhibits small fluctuations around a stable value. It is becoming increasingly clear that these fluctuations, in most free-running healthy systems, are not simply due to uncorrelated random errors and possess interesting properties, one of which is the property of fractal dynamics. Fractal dynamics model temporal processes in which similar patterns occur across multiple timescales of measurement. Smaller copies of a pattern are nested within larger copies of the pattern, a property termed scale invariance. It is an intriguing process that may deserve attention for implementing curricular development for students to reconsider homeostasis. Teaching fractal dynamics needs to make calculating resources available for students. The present paper offers a calculating resource that uses a basic formula and is executable in a simple spreadsheet. The spreadsheet allows computing detrended fluctuation analysis (DFA), the most frequently used method in the literature to quantify the fractal-scaling index of a physiological time series. DFA has been nicely described by the group at Harvard that designed it; the authors made the C language source available. Going further, it is suggested here that a guide to build DFA step by step in a spreadsheet has many advantages for teaching fractal physiology and beyond: 1) it promotes the DIY (do-it-yourself) in students and highlights scaling concepts; and 2) it makes DFA available for people not familiarized with executing code in C language.
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Dissertations / Theses on the topic "Detrended fractal analysis"

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Jaskowak, Daniel Joseph. "Detecting Transient Changes in Gait Using Fractal Scaling of Gait Variability in Conjunction with Gaussian Continuous Wavelet Transform." Thesis, Virginia Tech, 2019. http://hdl.handle.net/10919/87393.

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Accelerometer data can be analyzed using a variety of methods which are effective in the clinical setting. Time-series analysis is used to analyze spatiotemporal variables in various populations. More recently, investigators have focused on gait complexity and the structure of spatiotemporal variations during walking and running. This study evaluated the use of time-series analyses to determine gait parameters during running. Subjects were college-age female soccer players. Accelerometer data were collected using GPS-embedded trunk-mounted accelerometers. Customized Matlab® programs were developed that included Gaussian continuous wavelet transform (CWT) to determine spatiotemporal characteristics, detrended fluctuation analysis (DFA) to examine gait complexity and autocorrelation analyses (ACF) to assess gait regularity. Reliability was examined using repeated running efforts and intraclass correlation. Proof of concept was determined by examining differences in each variable between various running speeds. Applicability was established by examining gait before and after fatiguing activity. The results showed most variables had excellent reliability. Test-retest R2 values for these variables ranged from 0.8 to 1.0. Low reliability was seen in bilateral comparisons of gait symmetry. Increases in running speed resulted in expected changes in spatiotemporal and acceleration variables. Fatiguing exercise had minimal effects on spatiotemporal variables but resulted in noticeable declines in complexity. This investigation shows that GPS-embedded trunk-mounted accelerometers can be effectively used to assess running gait. CWT and DFA yield reliable measures of spatiotemporal characteristics of gait and gait complexity. The effects of running speed and fatigue on these variables provides proof of concepts and applicability for this analytical approach.
Master of Science
Fitness trackers have become widely accessible and easy to use. So much so that athletic teams have been using them to track activity throughout the season. Researchers are able to manipulate data generated from the fitness monitors to assess many different variables including gait. Monitoring gait may generate important information about the condition of the individual. As a person fatigues, running form is theorized to breakdown, which increases injury risk. Therefore the ability to monitor gait may be advantageous in preventing injury. The purpose of this study is to show that the methods in this study are reproducible, respond reasonably to changes in speed, and to observe the changes of gait in the presence of fatigue or on tired legs. Three analyses are used in this study. The first method called autocorrelation, overlays acceleration signals of consecutive foot strikes, and determines the similarity between them. The second method utilizes a wave transformation technique that is able to determine foot contact times. The final method attempts to determine any pattern in the running stride. This method looks for changes in the structure of the pattern. Less structure would indicate a stride that is fatigued. The results showed that the methods of gait analysis used in this study were reproducible and responded appropriately with changes in speed. Small changes in gait were observed due to the presence of fatigue. Further investigation into the use of these methods to determine changes in gait due to the presence of fatigue are warranted.
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Scipioni, Angel. "Contribution à la théorie des ondelettes : application à la turbulence des plasmas de bord de Tokamak et à la mesure dimensionnelle de cibles." Thesis, Nancy 1, 2010. http://www.theses.fr/2010NAN10125.

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La nécessaire représentation en échelle du monde nous amène à expliquer pourquoi la théorie des ondelettes en constitue le formalisme le mieux adapté. Ses performances sont comparées à d'autres outils : la méthode des étendues normalisées (R/S) et la méthode par décomposition empirique modale (EMD).La grande diversité des bases analysantes de la théorie des ondelettes nous conduit à proposer une approche à caractère morphologique de l'analyse. L'exposé est organisé en trois parties.Le premier chapitre est dédié aux éléments constitutifs de la théorie des ondelettes. Un lien surprenant est établi entre la notion de récurrence et l'analyse en échelle (polynômes de Daubechies) via le triangle de Pascal. Une expression analytique générale des coefficients des filtres de Daubechies à partir des racines des polynômes est ensuite proposée.Le deuxième chapitre constitue le premier domaine d'application. Il concerne les plasmas de bord des réacteurs de fusion de type tokamak. Nous exposons comment, pour la première fois sur des signaux expérimentaux, le coefficient de Hurst a pu être mesuré à partir d'un estimateur des moindres carrés à ondelettes. Nous détaillons ensuite, à partir de processus de type mouvement brownien fractionnaire (fBm), la manière dont nous avons établi un modèle (de synthèse) original reproduisant parfaitement la statistique mixte fBm et fGn qui caractérise un plasma de bord. Enfin, nous explicitons les raisons nous ayant amené à constater l'absence de lien existant entre des valeurs élevées du coefficient d'Hurst et de supposées longues corrélations.Le troisième chapitre est relatif au second domaine d'application. Il a été l'occasion de mettre en évidence comment le bien-fondé d'une approche morphologique couplée à une analyse en échelle nous ont permis d'extraire l'information relative à la taille, dans un écho rétrodiffusé d'une cible immergée et insonifiée par une onde ultrasonore
The necessary scale-based representation of the world leads us to explain why the wavelet theory is the best suited formalism. Its performances are compared to other tools: R/S analysis and empirical modal decomposition method (EMD). The great diversity of analyzing bases of wavelet theory leads us to propose a morphological approach of the analysis. The study is organized into three parts. The first chapter is dedicated to the constituent elements of wavelet theory. Then we will show the surprising link existing between recurrence concept and scale analysis (Daubechies polynomials) by using Pascal's triangle. A general analytical expression of Daubechies' filter coefficients is then proposed from the polynomial roots. The second chapter is the first application domain. It involves edge plasmas of tokamak fusion reactors. We will describe how, for the first time on experimental signals, the Hurst coefficient has been measured by a wavelet-based estimator. We will detail from fbm-like processes (fractional Brownian motion), how we have established an original model perfectly reproducing fBm and fGn joint statistics that characterizes magnetized plasmas. Finally, we will point out the reasons that show the lack of link between high values of the Hurst coefficient and possible long correlations. The third chapter is dedicated to the second application domain which is relative to the backscattered echo analysis of an immersed target insonified by an ultrasonic plane wave. We will explain how a morphological approach associated to a scale analysis can extract the diameter information
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Wang, Yi-En, and 王以恩. "The Detection of multi-fractal structure In Foreign Exchange Rate Market By Using Detrended Fluctuation Analysis." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/47505770414295541941.

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碩士
銘傳大學
財務金融學系碩士班
101
In the natural science， there are many ways for quantitative analysis in knowing the state of series. In the situation， Detrended fluctuation analysis， DFA， is one coming from fractal theory and is practical in the series of fractal and long memory. This tool is good at detect unstable series changing. We expect to provide the trace for system financial theory to introduce new models. The aim of the analysis is to understand the exchange rate by using Detrended fluctuation analysis， DFA， for analyzing the reality of the state in Taiwan. By observing the change of Hurst exponent， we can understand the difference between countries in multi-fractal characteristic and multi-fractal fluctuation.
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Santos, Maria Marta Oliveira Antunes dos. "Study of the electromyographic signal dynamic behavior in Amyotrophic Lateral Sclerosis (ALS)." Master's thesis, 2014. http://hdl.handle.net/10362/14461.

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Amyotrophic Lateral Sclerosis (ALS) is a neurodegenerative disease characterized by motor neurons degeneration, which reduces muscular force, being very difficult to diagnose. Mathematical methods are used in order to analyze the surface electromiographic signal’s dynamic behavior (Fractal Dimension (FD) and Multiscale Entropy (MSE)), evaluate different muscle group’s synchronization (Coherence and Phase Locking Factor (PLF)) and to evaluate the signal’s complexity (Lempel-Ziv (LZ) techniques and Detrended Fluctuation Analysis (DFA)). Surface electromiographic signal acquisitions were performed in upper limb muscles, being the analysis executed for instants of contraction for ipsilateral acquisitions for patients and control groups. Results from LZ, DFA and MSE analysis present capability to distinguish between the patient group and the control group, whereas coherence, PLF and FD algorithms present results very similar for both groups. LZ, DFA and MSE algorithms appear then to be a good measure of corticospinal pathways integrity. A classification algorithm was applied to the results in combination with extracted features from the surface electromiographic signal, with an accuracy percentage higher than 70% for 118 combinations for at least one classifier. The classification results demonstrate capability to distinguish members between patients and control groups. These results can demonstrate a major importance in the disease diagnose, once surface electromyography (sEMG) may be used as an auxiliary diagnose method.
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Book chapters on the topic "Detrended fractal analysis"

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Subhakar, D., and E. Chandrasekhar. "Detrended Fluctuation Analysis of Geophysical Well-Log Data." In Fractal Solutions for Understanding Complex Systems in Earth Sciences, 47–65. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-24675-8_4.

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Padhy, Simanchal. "The Multi-fractal Scaling Behavior of Seismograms Based on the Detrended Fluctuation Analysis." In Fractal Solutions for Understanding Complex Systems in Earth Sciences, 99–115. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-24675-8_7.

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Roozbehi, Zahra, Mahsa Mohaghegh, Hossein Lanjanian, and Peyman Hassani Abharian. "Proposing Two Different Feature Extraction Methods from Multi-fractal Detrended Fluctuation Analysis of Electroencephalography Signals: A Case Study on Attention-Deficit Hyperactivity Disorder." In Communications in Computer and Information Science, 796–803. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-63823-8_90.

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Lawal Kane, Ibrahim, and Venkatesan Madha Suresh. "Fractal Scaling Properties in Rainfall Time Series: A Case of Thiruvallur District, Tamil Nadu, India." In Groundwater Management and Resources. IntechOpen, 2021. http://dx.doi.org/10.5772/intechopen.100631.

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In the present study, the features of rainfall time series (1971–2016) in 9 meteorological regions of Thiruvallur, Tamil Nadu, India that comprises Thiruvallur, Korattur_Dam, Ponneri, Poondi, Red Hills, Sholingur, Thamaraipakkam, Thiruvottiyur and Vallur Anicut were studied. The evaluation of rainfall time series is one of the approaches for efficient hydrological structure design. Characterising and identifying patterns is one of the main objectives of time series analysis. Rainfall is a complex phenomenon, and the temporal variation of this natural phenomenon has been difficult to characterise and quantify due to its randomness. Such dynamical behaviours are present in multiple domains and it is therefore essential to have tools to model them. To solve this problem, fractal analysis based on Detrended Fluctuation Analysis (DFA) and Rescaled Range (R/S) analysis were employed. The fractal analysis produces estimates of the magnitude of detrended fluctuations at different scales (window sizes) of a time series and assesses the scaling relationship between estimates and time scales. The DFA and (R/S) gives an estimate known as Hurst exponent (H) that assumes self-similarity in the time series. The results of H exponent reveals typical behaviours shown by all the rainfall time series, Thiruvallur and Sholingur rainfall region have H exponent values within 0.5 < H < 1 which is an indication of persistent behaviour or long memory. In this case, a future data point is likely to be followed by a data point preceding it; Ponneri and Poondi have conflicting results based on the two methods, however, their H values are approximately 0.5 showing random walk behaviour in which there is no correlation between any part and a future. Thamaraipakkam, Thiruvottiyur, Vallur Anicut, Korattur Dam and Red Hills have H values less than 0.5 indicating a property called anti-persistent in which an increase will tend to be followed by a decrease or vice versa. Taking into consideration of such features in modelling, rainfall time series could be an exhaustive rainfall model. Finding appropriate models to estimate and predict future rainfalls is the core idea of this study for future research.
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Conference papers on the topic "Detrended fractal analysis"

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Yang, Rui, Xiangyang Li, and Junfeng Qi. "Application of Multi-Fractal Detrended Fluctuation Analysis." In 2015 International Conference on Education Technology, Management and Humanities Science (ETMHS 2015). Paris, France: Atlantis Press, 2015. http://dx.doi.org/10.2991/etmhs-15.2015.249.

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El-Amir, Mona M., Walid Al-Atabany, and Mohamed A. Eldosoky. "Emotion Recognition via Detrended Fluctuation Analysis and Fractal Dimensions." In 2019 36th National Radio Science Conference (NRSC). IEEE, 2019. http://dx.doi.org/10.1109/nrsc.2019.8734620.

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Gao, Jianbo, Jing Hu, and Wen-wen Tung. "Facilitating Joint Chaos and Fractal Analysis of Biosignals Through Nonlinear Adaptive Filtering." In ASME 2011 Dynamic Systems and Control Conference and Bath/ASME Symposium on Fluid Power and Motion Control. ASMEDC, 2011. http://dx.doi.org/10.1115/dscc2011-6083.

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Chaos and random fractal theories are among the most important for fully characterizing nonlinear dynamics of complicated multiscale biosignals. Chaos analysis requires that signals be relatively noise-free and stationary, while fractal analysis demands signals to be non-rhythmic and scale-free. To facilitate joint chaos and fractal analysis of biosignals, we report an adaptive multiscale decomposition algorithm, which: (1) can readily remove nonstationarities from the signal, (2) can more effectively reduce noise in the signals than linear filters, wavelet denoising, and chaos-based noise reduction schemes; (3) can readily decompose a multiscale biosignal into a series of intrinsically bandlimited functions; (4) offers a new formulation of fractal and multifractal analysis that is better than the popular detrended fluctuation analysis when a biosignal contains a strong oscillatory component. The effectiveness of the approach is demonstrated by applying it to classify EEGs for the purpose of detecting epileptic seizures.
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Matic, Zoran, Aleksandar Kalauzi, Mirjana Platisa, and Tijana Bojic. "Sensitivity Estimations in Favor of Using Inter-fractal Angle in Detrended Fluctuation Analysis." In 2022 12th Conference of the European Study Group on Cardiovascular Oscillations (ESGCO). IEEE, 2022. http://dx.doi.org/10.1109/esgco55423.2022.9931387.

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Calleja, Ramón Alejandro Gutiérrez, José Alberto Zamora Justo, and Alejandro Muñoz Diosdado. "Nonlinear dynamics methods for tachogram series analysis based on detrended fluctuation analysis and Higuchi’s fractal dimension." In MEDICAL PHYSICS: Fourteenth Mexican Symposium on Medical Physics. Author(s), 2016. http://dx.doi.org/10.1063/1.4954139.

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Marri, Kiran, and Ramakrishnan Swaminathan. "Classification of Muscular Nonfatigue and Fatigue Conditions Using Surface EMG Signals and Fractal Algorithms." In ASME 2016 Dynamic Systems and Control Conference. American Society of Mechanical Engineers, 2016. http://dx.doi.org/10.1115/dscc2016-9828.

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The application of surface electromyography (sEMG) technique for muscle fatigue studies is gaining importance in the field of clinical rehabilitation and sports medicine. These sEMG signals are highly nonstationary and exhibit scale-invariant self-similarity structure. The fractal analysis can estimate the scale invariance in the form of fractal dimension (FD) using monofractal (global single FD) or multifractal (local varying FD) algorithms. A comprehensive study of sEMG signal for muscle fatigue using both multifractal and monofractal FD features have not been established in the literature. In this work, an attempt has been made to differentiate sEMG signals recorded nonfatigue and fatigue conditions using monofractal and multifractal algorithms, and machine learning methods. For this purpose, sEMG signals have been recorded from biceps brachii muscles of fifty eight healthy subjects using a standard protocol. The signals of nonfatigue and fatigue region were subjected to eight monofractal (Higuchi, Katz, Petrosian, Sevcik, box counting, multi-resolution length, Hurst and power spectrum density) and two multifractal (detrended fluctuating and detrended moving average) algorithms and 28 FD features were extracted. The features were ranked using conventional and genetic algorithms, and a subset of FD features were further subjected to Naïve Bayes (NB), Logistic Regression (LR) and Multilayer Perceptron (MLP) classifiers. The results show that all fractal features are statistically significant. The classification accuracy using feature subset of conventional method is observed to be from 83% to 88%. The highest accuracy of 93.96% was achieved using genetic algorithm and LR classifier combination. The result demonstrated that the performance of multifractal FD features to be more suitable for sEMG signals as compared to monofractal FD features. The fractal analysis of sEMG signals appears to be a very promising biomarker for muscle fatigue classification and can be extended to detection of fatigue onset in varied neuromuscular conditions.
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Gao, Jianbo, Yi Zheng, and Jing Hu. "Long-Range Temporal Correlations, Multifractality, and the Causal Relation Between Neural Inputs and Movements." In ASME 2011 Dynamic Systems and Control Conference and Bath/ASME Symposium on Fluid Power and Motion Control. ASMEDC, 2011. http://dx.doi.org/10.1115/dscc2011-6081.

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Understanding the causal relation between neural inputs and movements is very important for the success of brain machine interfaces (BMIs). In this study, we perform systematic statistical and information theoretical analysis of neuronal firings of 104 neurons, and employ three different types of fractal and multifractal techniques (including Fano factor analysis, multifractal detrended fluctuation analysis (MF-DFA), and wavelet multifractal analysis) to examine whether neuronal firings related to movements may have long-range temporal correlations. We find that MF-DFA and wavelet multifractal analysis (but not Fano factor analysis) clearly indicate that when neuronal firings are not well correlated with movement trajectory, they do not have or only have weak temporal correlations. When neuronal firings are well correlated with movements, they are characterized by very strong temporal correlations, up to a time scale comparable to the average time between two successive reaching tasks. This suggests that neurons well correlated with hand trajectory experienced a “re-setting” effect at the start of each reaching task. We further discuss the significance of the coalition of those important neurons in executing cortical control of prostheses.
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Wan, Li, Jianxia Wang, and Jiaoxiu Ling. "Fractional Behavior in Element Grade Series: An Application of Detrended Fluctuation Analysis." In 2011 Fourth International Workshop on Chaos-Fractals Theories and Applications (IWCFTA). IEEE, 2011. http://dx.doi.org/10.1109/iwcfta.2011.65.

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Reports on the topic "Detrended fractal analysis"

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Bielinskyi, Andriy, Serhiy Semerikov, Oleksandr Serdiuk, Victoria Solovieva, Vladimir Soloviev, and Lukáš Pichl. Econophysics of sustainability indices. [б. в.], October 2020. http://dx.doi.org/10.31812/123456789/4118.

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In this paper, the possibility of using some econophysical methods for quantitative assessment of complexity measures: entropy (Shannon, Approximate and Permutation entropies), fractal (Multifractal detrended fluctuation analysis – MF-DFA), and quantum (Heisenberg uncertainty principle) is investigated. Comparing the capability of both entropies, it is obtained that both measures are presented to be computationally efficient, robust, and useful. Each of them detects patterns that are general for crisis states. The similar results are for other measures. MF-DFA approach gives evidence that Dow Jones Sustainability Index is multifractal, and the degree of it changes significantly at different periods. Moreover, we demonstrate that the quantum apparatus of econophysics has reliable models for the identification of instability periods. We conclude that these measures make it possible to establish that the socially responsive exhibits characteristic patterns of complexity, and the proposed measures of complexity allow us to build indicators-precursors of critical and crisis phenomena.
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