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| detrended fluctuation analysis example: Time Series Analysis Wilfredo Palma, 2016-03-07 A modern and accessible guide to the analysis of introductory time series data Featuring an organized and self-contained guide, Time Series Analysis provides a broad introduction to the most fundamental methodologies and techniques of time series analysis. The book focuses on the treatment of univariate time series by illustrating a number of well-known models such as ARMA and ARIMA. Providing contemporary coverage, the book features several useful and newlydeveloped techniques such as weak and strong dependence, Bayesian methods, non-Gaussian data, local stationarity, missing values and outliers, and threshold models. Time Series Analysis includes practical applications of time series methods throughout, as well as: Real-world examples and exercise sets that allow readers to practice the presented methods and techniques Numerous detailed analyses of computational aspects related to the implementation of methodologies including algorithm efficiency, arithmetic complexity, and process time End-of-chapter proposed problems and bibliographical notes to deepen readers’ knowledge of the presented material Appendices that contain details on fundamental concepts and select solutions of the problems implemented throughout A companion website with additional data fi les and computer codes Time Series Analysis is an excellent textbook for undergraduate and beginning graduate-level courses in time series as well as a supplement for students in advanced statistics, mathematics, economics, finance, engineering, and physics. The book is also a useful reference for researchers and practitioners in time series analysis, econometrics, and finance. Wilfredo Palma, PhD, is Professor of Statistics in the Department of Statistics at Pontificia Universidad Católica de Chile. He has published several refereed articles and has received over a dozen academic honors and awards. His research interests include time series analysis, prediction theory, state space systems, linear models, and econometrics. He is the author of Long-Memory Time Series: Theory and Methods, also published by Wiley. |
| detrended fluctuation analysis example: Biosignal and Medical Image Processing John L. Semmlow, Benjamin Griffel, 2021-10-01 Written specifically for biomedical engineers, Biosignal and Medical Image Processing, Third Edition provides a complete set of signal and image processing tools, including diagnostic decision-making tools, and classification methods. Thoroughly revised and updated, it supplies important new material on nonlinear methods for describing and classify |
| detrended fluctuation analysis example: Nonlinear Dynamical Systems Analysis for the Behavioral Sciences Using Real Data Stephen J. Guastello, Robert A.M. Gregson, 2016-04-19 Although its roots can be traced to the 19th century, progress in the study of nonlinear dynamical systems has taken off in the last 30 years. While pertinent source material exists, it is strewn about the literature in mathematics, physics, biology, economics, and psychology at varying levels of accessibility. A compendium research methods reflect |
| detrended fluctuation analysis example: Biomechanics and Gait Analysis Nicholas Stergiou, 2020-03-25 Biomechanics and Gait Analysis presents a comprehensive book on biomechanics that focuses on gait analysis. It is written primarily for biomedical engineering students, professionals and biomechanists with a strong emphasis on medical devices and assistive technology, but is also of interest to clinicians and physiologists. It allows novice readers to acquire the basics of gait analysis, while also helping expert readers update their knowledge. The book covers the most up-to-date acquisition and computational methods and advances in the field. Key topics include muscle mechanics and modeling, motor control and coordination, and measurements and assessments. This is the go to resource for an understanding of fundamental concepts and how to collect, analyze and interpret data for research, industry, clinical and sport. - Details the fundamental issues leading to the biomechanical analyses of gait and posture - Covers the theoretical basis and practical aspects associated with gait analysis - Presents methods and tools used in the field, including electromyography, signal processing and spectral analysis, amongst others |
| detrended fluctuation analysis example: Computational Intelligence-based Time Series Analysis Dinesh C. S. Bisht, Mangey Ram, 2022-11-30 The sequential analysis of data and information gathered from past to present is called time series analysis. Time series data are of high dimension, large size and updated continuously. A time series depends on various factors like trend, seasonality, cycle and irregular data set, and is basically a series of data points well-organized in time. Time series forecasting is a significant area of machine learning. There are various prediction problems that are time-dependent and these problems can be handled through time series analysis. Computational intelligence (CI) is a developing computing approach for the forthcoming several years. CI gives the litheness to model the problem according to given requirements. It helps to find swift solutions to the problems arising in numerous disciplines. These methods mimic human behavior. The main objective of CI is to develop intelligent machines to provide solutions to real world problems, which are not modelled or are too difficult to model mathematically. This book aims to cover the recent advances in time series and applications of CI for time series analysis. |
| detrended fluctuation analysis example: Nonlinear Analysis for Human Movement Variability Nicholas Stergiou, 2018-09-03 How Does the Body’s Motor Control System Deal with Repetition? While the presence of nonlinear dynamics can be explained and understood, it is difficult to be measured. A study of human movement variability with a focus on nonlinear dynamics, Nonlinear Analysis for Human Movement Variability, examines the characteristics of human movement within this framework, explores human movement in repetition, and explains how and why we analyze human movement data. It takes an in-depth look into the nonlinear dynamics of systems within and around us, investigates the temporal structure of variability, and discusses the properties of chaos and fractals as they relate to human movement. Providing a foundation for the use of nonlinear analysis and the study of movement variability in practice, the book describes the nonlinear dynamical features found in complex biological and physical systems, and introduces key concepts that help determine and identify patterns within the fluctuations of data that are repeated over time. It presents commonly used methods and novel approaches to movement analysis that reveal intriguing properties of the motor control system and introduce new ways of thinking about variability, adaptability, health, and motor learning. In addition, this text: Demonstrates how nonlinear measures can be used in a variety of different tasks and populations Presents a wide variety of nonlinear tools such as the Lyapunov exponent, surrogation, entropy, and fractal analysis Includes examples from research on how nonlinear analysis can be used to understand real-world applications Provides numerous case studies in postural control, gait, motor control, and motor development Nonlinear Analysis for Human Movement Variability advances the field of human movement variability research by dissecting human movement and studying the role of movement variability. The book proposes new ways to use nonlinear analysis and investigate the temporal structure of variability, and enables engineers, movement scientists, clinicians, and those in related disciplines to effectively apply nonlinear analysis in practice. |
| detrended fluctuation analysis example: Multifractal Detrended Analysis Method and Its Application in Financial Markets Guangxi Cao, Ling-Yun He, Jie Cao, 2018-02-18 This book collects high-quality papers on the latest fundamental advances in the state of Econophysics and Management Science, providing insights that address problems concerning the international economy, social development and economic security. This book applies the multi-fractal detrended class method, and improves the method with different filters. The authors apply those methods to a variety of areas: financial markets, energy markets, gold market and so on. This book is arguably a systematic research and summary of various kinds of multi-fractal detrended methods. Furthermore, it puts forward some investment suggestions on a healthy development of financial markets. |
| detrended fluctuation analysis example: Practical Guide for Biomedical Signals Analysis Using Machine Learning Techniques Abdulhamit Subasi, 2019-03-16 Practical Guide for Biomedical Signals Analysis Using Machine Learning Techniques: A MATLAB Based Approach presents how machine learning and biomedical signal processing methods can be used in biomedical signal analysis. Different machine learning applications in biomedical signal analysis, including those for electrocardiogram, electroencephalogram and electromyogram are described in a practical and comprehensive way, helping readers with limited knowledge. Sections cover biomedical signals and machine learning techniques, biomedical signals, such as electroencephalogram (EEG), electromyogram (EMG) and electrocardiogram (ECG), different signal-processing techniques, signal de-noising, feature extraction and dimension reduction techniques, such as PCA, ICA, KPCA, MSPCA, entropy measures, and other statistical measures, and more. This book is a valuable source for bioinformaticians, medical doctors and other members of the biomedical field who need a cogent resource on the most recent and promising machine learning techniques for biomedical signals analysis. - Provides comprehensive knowledge in the application of machine learning tools in biomedical signal analysis for medical diagnostics, brain computer interface and man/machine interaction - Explains how to apply machine learning techniques to EEG, ECG and EMG signals - Gives basic knowledge on predictive modeling in biomedical time series and advanced knowledge in machine learning for biomedical time series |
| detrended fluctuation analysis example: The Weather and Climate Shaun Lovejoy, Daniel Schertzer, 2013-04-04 A new method of modeling the atmosphere, synthesizing data analysis techniques and multifractal statistics, for atmospheric researchers and graduate students. |
| detrended fluctuation analysis example: Criticality in neural network behavior and its implications for computational processing in healthy and perturbed conditions Axel Sandvig, Matteo Caleo, Ioanna Sandvig, 2023-02-03 |
| detrended fluctuation analysis example: Time Series Analysis in Seismology Alejandro Ramírez-Rojas, Leonardo Di G. Sigalotti, Elsa Leticia Flores Márquez, Otto Rendón, 2019-08-02 Time Series Analysis in Seismology: Practical Applications provides technical assistance and coverage of available methods to professionals working in the field of seismology. Beginning with a thorough review of open problems in geophysics, including tectonic plate dynamics, localization of solitons, and forecasting, the book goes on to describe the various types of time series or punctual processes obtained from those systems. Additionally, the book describes a variety of methods and techniques relating to seismology and includes a discussion of future developments and improvements. Time Series Analysis in Seismology offers a concise presentation of the most recent advances in the analysis of geophysical data, particularly with regard to seismology, making it a valuable tool for researchers and students working in seismology and geophysics. Presents the necessary tools for time series analysis as it relates to seismology in a compact and consistent manner Includes a discussion of technical resources that can be applied to time series data analysis across multiple disciplines Describes the methods and techniques available for solving problems related to the analysis of complex data sets Provides exercises at the end of each chapter to enhance comprehension |
| detrended fluctuation analysis example: Nonlinearity in Living Systems: Theoretical and Practical Perspectives on Metrics of Physiological Signal Complexity Sladjana Spasić, Srdjan Kesić, 2019-06-28 The biological basis of physiological signals is incredibly complex. While many types of research certainly appreciate molecular, cellular and systems approach to unravel overall biological complexity, in the recent decades the interest for mathematical and computational characterization of structural and functional basis underlying biological phenomena gain wide popularity among scientists. Nowadays, we witnessed wide range applications of nonlinear quantitative analysis that produced measures such as fractal dimension, power-law scaling, Hurst exponent, Lyapunov exponent, approximate entropy, sample entropy, Lempel–Ziv complexity, as well as other metrics for predictions of onset and progression of many pathological conditions, especially in the central nervous systems (CNS). In this Research Topic, we seek to bring together the recent practical and theoretical advances in the development and application of nonlinear methods or narrower fractal-based methods for characterizing the complex physiological systems at multiple levels of the organization. We will discuss the use of various complexity measures and appropriate parameters for characterizing the variety of physiological signals up to the systems level. There are multiple aims in this topic. The recent advancement in the application of nonlinear methods for both normal and pathological physiological conditions is the first. The second aim is to emphasize the more recent successful attempt to apply these methods across animal species. Finally, a comprehensive understanding of advantages and disadvantages of each method, especially between its mathematical assumptions and real-world applicability, can help to find out what is at stake regarding the above aims and to direct us toward the more fruitful application of nonlinear measures and statistics in physiology and biology in general. |
| detrended fluctuation analysis example: Thermoacoustic Instability R. I. Sujith, Samadhan A. Pawar, 2021-12-14 This book systematically presents the consolidated findings of the phenomenon of self-organization observed during the onset of thermoacoustic instability using approaches from dynamical systems and complex systems theory. Over the last decade, several complex dynamical states beyond limit cycle oscillations such as quasiperiodicity, frequency-locking, period-n, chaos, strange non-chaos, and intermittency have been discovered in thermoacoustic systems operated in laminar and turbulent flow regimes. During the onset of thermoacoustic instability in turbulent systems, an ordered acoustic field and large coherent vortices emerge from the background of turbulent combustion. This emergence of order from disorder in both temporal and spatiotemporal dynamics is explored in the contexts of synchronization, pattern formation, collective interaction, multifractality, and complex networks. For the past six decades, the spontaneous emergence of large amplitude, self-sustained, tonal oscillations in confined combustion systems, characterized as thermoacoustic instability, has remained one of the most challenging areas of research. The presence of such instabilities continues to hinder the development and deployment of high-performance combustion systems used in power generation and propulsion applications. Even with the advent of sophisticated measurement techniques to aid experimental investigations and vast improvements in computational power necessary to capture flow physics in high fidelity simulations, conventional reductionist approaches have not succeeded in explaining the plethora of dynamical behaviors and the associated complexities that arise in practical combustion systems. As a result, models and theories based on such approaches are limited in their application to mitigate or evade thermoacoustic instabilities, which continue to be among the biggest concerns for engine manufacturers today. This book helps to overcome these limitations by providing appropriate methodologies to deal with nonlinear thermoacoustic oscillations, and by developing control strategies that can mitigate and forewarn thermoacoustic instabilities. The book is also beneficial to scientists and engineers studying the occurrence of several other instabilities, such as flow-induced vibrations, compressor surge, aeroacoustics and aeroelastic instabilities in diverse fluid-mechanical environments, to graduate students who intend to apply dynamical systems and complex systems approach to their areas of research, and to physicists who look for experimental applications of their theoretical findings on nonlinear and complex systems. |
| detrended fluctuation analysis example: Synergetics and Fractals in Tribology Ahad Kh Janahmadov, Maksim Y Javadov, 2016-01-29 This book examines the theoretical and practical aspects of tribological process using synergy, fractal and multifractal methods, and the fractal and multifractal models of self-similar tribosystems developed on their basis. It provides a comprehensive analysis of their effectiveness, and also considers the method of flicker noise spectroscopy with detailed parameterization of surface roughness friction. All models, problems and solutions are taken and tested on the set of real-life examples of oil-gas industry. The book is intended for researchers, graduate students and engineers specialising in the field of tribology, and also for senior students of technical colleges. |
| detrended fluctuation analysis example: Fractal Analyses: Statistical And Methodological Innovations And Best Practices John G. Holden, Michael A. Riley, Jianbo Gao, Kjerstin Torre, 2013-06-03 Many statistical and methodological developments regarding fractal analyses have appeared in the scientific literature since the publication of the seminal texts introducing Fractal Physiology. However, the lion’s share of more recent work is distributed across many outlets and disciplines, including aquatic sciences, biology, computer science, ecology, economics, geology, mathematics, medicine, neuroscience, physics, physiology, psychology, and others. The purpose of this special topic is to solicit submissions regarding fractal and nonlinear statistical techniques from experts that span a wide range of disciplines. The articles will aggregate extensive cross-discipline expertise into comprehensive and broadly applicable resources that will support the application of fractal methods to physiology and related disciplines. The articles will be organized with respect to a continuum defined by the characteristics of the empirical measurements a given analysis is intended to confront. At one end of the continuum are stochastic techniques directed at assessing scale invariant but stochastic data. The next step in the continuum concerns self-affine random fractals and methods directed at systems that entail scale-invariant or 1/f patterns or related patterns of temporal and spatial fluctuation. Analyses directed at (noisy) deterministic signals correspond to the final stage of the continuum that relates the statistical treatments of nonlinear stochastic and deterministic signals. Each section will contain introductory articles, advanced articles, and application articles so readers with any level of expertise with fractal methods will find the special topic accessible and useful. Example stochastic methods include probability density estimation for the inverse power-law, the lognormal, and related distributions. Articles describing statistical issues and tools for discriminating different classes of distributions will be included. An example issue is distinguishing power-law distributions from exponential distributions. Modeling issues and problems regarding statistical mimicking will be addressed as well. The random fractal section will present introductions to several one-dimensional monofractal time-series analysis. Introductory articles will be accompanied by advanced articles that will supply comprehensive treatments of all the key fractal time series methods such as dispersion analysis, detrended fluctuation analysis, power spectral density analysis, and wavelet techniques. Box counting and related techniques will be introduced and described for spatial analyses of two and three dimensional domains as well. Tutorial articles on the execution and interpretation of multifractal analyses will be solicited. There are several standard wavelet based and detrended fluctuation based methods for estimating a multifractal spectrum. We hope to include articles that contrast the different methods and compare their statistical performance as well. The deterministic methods section will include articles that present methods of phase space reconstruction, recurrence analysis, and cross-recurrence analysis. Recurrence methods are widely applicable, but motivated by signals that contain deterministic patterns. Nonetheless recent developments such as the analysis of recurrence interval scaling relations suggest applicability to fractal systems. Several related statistical procedures will be included in this section. Examples include average mutual information statistics and false nearest neighbor analyses. |
| detrended fluctuation analysis example: Complexity and Nonlinearity in Cardiovascular Signals Riccardo Barbieri, Enzo Pasquale Scilingo, Gaetano Valenza, 2017-08-09 This book reports on the latest advances in complex and nonlinear cardiovascular physiology aimed at obtaining reliable, effective markers for the assessment of heartbeat, respiratory, and blood pressure dynamics. The chapters describe in detail methods that have been previously defined in theoretical physics such as entropy, multifractal spectra, and Lyapunov exponents, contextualized within physiological dynamics of cardiovascular control, including autonomic nervous system activity. Additionally, the book discusses several application scenarios of these methods. The text critically reviews the current state-of-the-art research in the field that has led to the description of dedicated experimental protocols and ad-hoc models of complex physiology. This text is ideal for biomedical engineers, physiologists, and neuroscientists. This book also: Expertly reviews cutting-edge research, such as recent advances in measuring complexity, nonlinearity, and information-theoretic concepts applied to coupled dynamical systems Comprehensively describes applications of analytic technique to clinical scenarios such as heart failure, depression and mental disorders, atrial fibrillation, acute brain lesions, and more Broadens readers' understanding of cardiovascular signals, heart rate complexity, heart rate variability, and nonlinear analysis |
| detrended fluctuation analysis example: Using Time Series to Analyze Long-Range Fractal Patterns Matthijs Koopmans, 2020-09-23 Using Time Series to Analyze Long Range Fractal Patterns presents methods for describing and analyzing dependency and irregularity in long time series. Irregularity refers to cycles that are similar in appearance, but unlike seasonal patterns more familiar to social scientists, repeated over a time scale that is not fixed. Until now, the application of these methods has mainly involved analysis of dynamical systems outside of the social sciences, but this volume makes it possible for social scientists to explore and document fractal patterns in dynamical social systems. Author Matthijs Koopmans concentrates on two general approaches to irregularity in long time series: autoregressive fractionally integrated moving average models, and power spectral density analysis. He demonstrates the methods through two kinds of examples: simulations that illustrate the patterns that might be encountered and serve as a benchmark for interpreting patterns in real data; and secondly social science examples such a long range data on monthly unemployment figures, daily school attendance rates; daily numbers of births to teens, and weekly survey data on political orientation. Data and R-scripts to replicate the analyses are available on an accompanying website. |
| detrended fluctuation analysis example: The Science of Disasters Armin Bunde, Jürgen Kropp, Hans-Joachim Schellnhuber, 2012-12-06 This book tackles these questions by applying advanced methods from statistical physics and related fields to all types of non-linear dynamics prone to disaster. It gives readers an insight into the problems of catastrophes and is one of the first books on the theories of disaster. Based on physical and mathematical theories, the general principles of disaster appearance are explained. |
| detrended fluctuation analysis example: Analysis, Geometry, Nonlinear Optimization And Applications Panos M Pardalos, Themistocles M Rassias, 2023-03-20 This volume features an extensive account of both research and expository papers in a wide area of engineering and mathematics and its various applications.Topics treated within this book include optimization of control points, game theory, equilibrium points, algorithms, Cartan matrices, integral inequalities, Volterra integro-differential equations, Caristi-Kirk theorems, Laplace type integral operators, etc.This useful reference text benefits graduate students, beginning research engineers and mathematicians as well as established researchers in these domains. |
| detrended fluctuation analysis example: Applied Mechanics and Mechanical Engineering III Xiong Zhou, Hong Hua Tan, 2012-12-13 Selected, peer reviewed papers from the 2012 3rd International Conference on Applied Mechanics and Mechanical Engineering (ICAMME 2012), November 14-15, 2012, Macau |
| detrended fluctuation analysis example: Control Performance Assessment: Theoretical Analyses and Industrial Practice Paweł D. Domański, 2019-09-01 This book presents a comprehensive review of currently available Control Performance Assessment methods. It covers a broad range of classical and modern methods, with a main focus on assessment practice, and is intended to help practitioners learn and properly perform control assessment in the industrial reality. Further, it offers an educational guide for control engineers, who are currently in high demand in the industry. The book consists of three main parts. Firstly, a comprehensive review of available approaches is presented and discussed. The classical canon methods are extended with a discussion of nonlinear and complex alternative measures using non-Gaussian statistics, persistence and fractional calculations. Secondly, the methods’ applicability aspects are visualized with the aid of computer simulations, covering the most popular control philosophies used in the process industry. Lastly, a critical review of the methods discussed, on the basis of real-world industrial examples, rounds out the coverage. |
| detrended fluctuation analysis example: Human and Machine Perception 3 Virginio Cantoni, Vito di Gesù, Alessandra Setti, Domenico Tegolo, 2012-12-06 The following are the proceedings of the Fourth International Workshop on Human and Machine Perception held in Palermo, Italy, on June 20 -23, 2000, under the auspices of three Institutions: the Cybernetic and Biophysics Group (GNCB) of the Italian National Research Council (CNR) and the two Inter-Department Centers of Cognitive Sciences of Palermo and Pavia University respectively. A broad spectrum of topics are covered in this series, ranging from computer perception to psychology and physiology of perception. The theme of this workshop on Human and Machine Perception was focused on Thinking, Deciding, and Acting. As in the past editions the final goal has been the analysis and the comparison of biological and artificial solutions. The focus of the lectures has been on presenting the state-of-the-art and outlining open questions. In particular, they sought to stress links, suggesting possible synergies between the different cultural areas. The panel discussion has been conceived as a forum for an open debate, briefly introduced by each panelist, and mainly aimed at deeper investigation of the different approaches to perception and strictly related topics. The panelists were asked to prepare a few statements on hot-points as a guide for discussion. These statements were delivered to the participants together with the final program, for a more qualified discussion. |
| detrended fluctuation analysis example: Fractal Patterns in Nonlinear Dynamics and Applications Santo Banerjee, M K Hassan, Sayan Mukherjee, A Gowrisankar, 2020-03-27 Most books on fractals focus on deterministic fractals as the impact of incorporating randomness and time is almost absent. Further, most review fractals without explaining what scaling and self-similarity means. This book introduces the idea of scaling, self-similarity, scale-invariance and their role in the dimensional analysis. For the first time, fractals emphasizing mostly on stochastic fractal, and multifractals which evolves with time instead of scale-free self-similarity, are discussed. Moreover, it looks at power laws and dynamic scaling laws in some detail and provides an overview of modern statistical tools for calculating fractal dimension and multifractal spectrum. |
| detrended fluctuation analysis example: Computational Intelligence in Healthcare Amit Kumar Manocha, Shruti Jain, Mandeep Singh, Sudip Paul, 2021-05-11 Artificial intelligent systems, which offer great improvement in healthcare sector assisted by machine learning, wireless communications, data analytics, cognitive computing, and mobile computing provide more intelligent and convenient solutions and services. With the help of the advanced techniques, now a days it is possible to understand human body and to handle & process the health data anytime and anywhere. It is a smart healthcare system which includes patient, hospital management, doctors, monitoring, diagnosis, decision making modules, disease prevention to meet the challenges and problems arises in healthcare industry. Furthermore, the advanced healthcare systems need to upgrade with new capabilities to provide human with more intelligent and professional healthcare services to further improve the quality of service and user experience. To explore recent advances and disseminate state-of-the-art techniques related to intelligent healthcare services and applications. This edited book involved in designing systems that will permit the societal acceptance of ambient intelligence including signal processing, imaging, computing, instrumentation, artificial intelligence, internet of health things, data analytics, disease detection, telemedicine, and their applications. As the book includes recent trends in research issues and applications, the contents will be beneficial to Professors, researchers, and engineers. This book will provide support and aid to the researchers involved in designing latest advancements in communication and intelligent systems that will permit the societal acceptance of ambient intelligence. This book presents the latest research being conducted on diverse topics in intelligence technologies with the goal of advancing knowledge and applications healthcare sector and to present the latest snapshot of the ongoing research as well as to shed further light on future directions in this space. The aim of publishing the book is to serve for educators, researchers, and developers working in recent advances and upcoming technologies utilizing computational sciences. |
| detrended fluctuation analysis example: Civil Engineering and Energy-Environment Vol 2 Qingfei Gao, Zhenhua Duan, 2023-06-16 Civil Engineering and Energy-Environment focuses on the research of civil engineering, environment resources and energy materials. This proceedings gathers the most cutting-edge research and achievements, aiming to provide scholars and engineers with preferable research direction and engineering solution as reference. Subjects in this proceedings include: - Engineering Structure - Environmental Protection Materials - Architectural Environment ·Environment Resources - Energy Storage - Building Electrical Engineering The works of this proceedings will promote development of civil engineering and environment engineering. Thereby, promote scientific information interchange between scholars from top universities, research centers and high-tech enterprises working all around the world. |
| detrended fluctuation analysis example: Neural Information Processing Bao-Liang Lu, Liqing Zhang, James Kwok, 2011-11-12 The three volume set LNCS 7062, LNCS 7063, and LNCS 7064 constitutes the proceedings of the 18th International Conference on Neural Information Processing, ICONIP 2011, held in Shanghai, China, in November 2011. The 262 regular session papers presented were carefully reviewed and selected from numerous submissions. The papers of part I are organized in topical sections on perception, emotion and development, bioinformatics, biologically inspired vision and recognition, bio-medical data analysis, brain signal processing, brain-computer interfaces, brain-like systems, brain-realistic models for learning, memory and embodied cognition, Clifford algebraic neural networks, combining multiple learners, computational advances in bioinformatics, and computational-intelligent human computer interaction. The second volume is structured in topical sections on cybersecurity and data mining workshop, data mining and knowledge doscovery, evolutionary design and optimisation, graphical models, human-originated data analysis and implementation, information retrieval, integrating multiple nature-inspired approaches, kernel methods and support vector machines, and learning and memory. The third volume contains all the contributions connected with multi-agent systems, natural language processing and intelligent Web information processing, neural encoding and decoding, neural network models, neuromorphic hardware and implementations, object recognition, visual perception modelling, and advances in computational intelligence methods based pattern recognition. |
| detrended fluctuation analysis example: Acute and chronic changes in postural control in response to different physiological states and external environmental conditions Nejc Sarabon, Jan Babic, Urs Granacher, Thierry Paillard, 2023-04-04 |
| detrended fluctuation analysis example: Guide To Temporal Networks, A (Second Edition) Naoki Masuda, Renaud Lambiotte, 2020-10-05 Network science offers a powerful language to represent and study complex systems composed of interacting elements — from the Internet to social and biological systems. A Guide to Temporal Networks presents recent theoretical and modelling progress in the emerging field of temporally varying networks and provides connections between the different areas of knowledge required to address this multi-disciplinary subject. After an introduction to key concepts on networks and stochastic dynamics, the authors guide the reader through a coherent selection of mathematical and computational tools for network dynamics. Perfect for students and professionals, this book is a gateway to an active field of research developing between the disciplines of applied mathematics, physics and computer science, with applications in others including social sciences, neuroscience and biology.This second edition extensively expands upon the coverage of the first edition as the authors expertly present recent theoretical and modelling progress in the emerging field of temporal networks, providing the keys to (and connections between) the different areas of knowledge required to address this multi-disciplinary problem. |
| detrended fluctuation analysis example: Fractal Physiology James B Bassingthwaighte, Larry S Liebovitch, Bruce J West, 2013-05-27 I know that most men, including those at ease with the problems of the greatest complexity, can seldom accept even the simplest and most obvious truth if it be such as would oblige them to admit the falsity of conclusions which they have delighted in explaining to colleagues, which they have proudly taught to others, and which they have woven, thread by thread, into the fabric of their lives. Joseph Ford quoting Tolstoy (Gleick, 1987) We are used to thinking that natural objects have a certain form and that this form is determined by a characteristic scale. If we magnify the object beyond this scale, no new features are revealed. To correctly measure the properties of the object, such as length, area, or volume, we measure it at a resolution finer than the characteristic scale of the object. We expect that the value we measure has a unique value for the object. This simple idea is the basis of the calculus, Euclidean geometry, and the theory of measurement. However, Mandelbrot (1977, 1983) brought to the world's attention that many natural objects simply do not have this preconceived form. Many of the structures in space and processes in time of living things have a very different form. Living things have structures in space and fluctuations in time that cannot be characterized by one spatial or temporal scale. They extend over many spatial or temporal scales. |
| detrended fluctuation analysis example: Artificial Intelligence and Soft Computing Leszek Rutkowski, Rafał Scherer, Marcin Korytkowski, Witold Pedrycz, Ryszard Tadeusiewicz, Jacek M. Zurada, 2023-01-23 The two-volume set LNAI 13588 and 13589 constitutes the refereed post-conference proceedings of the 21st International Conference on Artificial Intelligence and Soft Computing, ICAISC 2022, held in Zakopane, Poland, during June 19–23, 2022. The 69 revised full papers presented in these proceedings were carefully reviewed and selected from 161 submissions. The papers are organized in the following topical sections: Volume I: Neural networks and their applications; fuzzy systems and their applications; evolutionary algorithms and their applications; pattern classification; artificial intelligence in modeling and simulation. Volume II: Computer vision, image and speech analysis; data mining; various problems of artificial intelligence; bioinformatics, biometrics and medical applications. |
| detrended fluctuation analysis example: The Tools of Neuroscience Experiment John Bickle, Carl F. Craver, Ann-Sophie Barwich, 2021-12-31 This volume establishes the conceptual foundation for sustained investigation into tool development in neuroscience. Neuroscience relies on diverse and sophisticated experimental tools, and its ultimate explanatory target—our brains and hence the organ driving our behaviors—catapults the investigation of these research tools into a philosophical spotlight. The chapters in this volume integrate the currently scattered work on tool development in neuroscience into the broader philosophy of science community. They also present an accessible compendium for neuroscientists interested in the broader theoretical dimensions of their experimental practices. The chapters are divided into five thematic sections. Section 1 discusses the development of revolutionary research tools across neuroscience’s history and argues to various conclusions concerning the relationship between new research tools and theory progress in neuroscience. Section 2 shows how a focus on research tools and their development in neuroscience transforms some traditional epistemological issues and questions about knowledge production in philosophy of science. Section 3 speaks to the most general questions about the way we characterize the nature of the portion of the world that this science addresses. Section 4 discusses hybrid research tools that integrate laboratory and computational methods in exciting new ways. Finally, Section 5 extends research on tool development to the related science of genetics. The Tools of Neuroscience Experiment will be of interest to philosophers and philosophically minded scientists working at the intersection of philosophy and neuroscience. |
| detrended fluctuation analysis example: Mathematics as a Laboratory Tool John Milton, Toru Ohira, 2014-09-18 This introductory textbook is based on the premise that the foundation of good science is good data. The educational challenge addressed by this introductory textbook is how to present a sampling of the wide range of mathematical tools available for laboratory research to well-motivated students with a mathematical background limited to an introductory course in calculus. |
| detrended fluctuation analysis example: Practical Approach to Electroencephalography Mark H. Libenson, 2009-12-04 Why consult encyclopedic references when you only need the essentials? Practical Approach to Electroencephalography, by Mark H. Libenson, MD, equips you with just the right amount of guidance you need for obtaining optimal EEG results! It presents a thorough but readable guide to EEGs, explaining what to do, what not to do, what to look for, and how to interpret the results. It also goes beyond the technical aspects of performing EEGs by providing case studies of the neurologic disorders and conditions in which EEGs are used, making this an excellent learning tool. Abundant EEG examples throughout help you to recognize normal and abnormal EEGs in all situations. - Presents enough detail and answers to questions and problems encountered by the beginner and the non-expert. - Uses abundant EEG examples to help you recognize normal and abnormal EEGs in all situations. - Provides expert pearls from Dr. Libenson that guide you in best practices in EEG testing. - Features a user-friendly writing style from a single author that makes learning easy. - Examines the performance of EEGs—along with the disorders for which they're performed—for a resource that considers the patient and not just the technical aspects of EEGs. - Includes discussions of various disease entities, like epilepsy, in which EEGs are used, as well as other special issues, to equip you to handle more cases. |
| detrended fluctuation analysis example: Circuits, Signals, and Systems for Bioengineers John Semmlow, 2024-07-19 Circuits, Signals, and Systems for Bioengineers: A MATLAB-Based Introduction, Fourth Edition, guides the reader through the electrical engineering principles that can be applied to biological systems. It details the basic engineering concepts that underlie biomedical systems, medical devices, biocontrol, and biomedical signal analysis, providing a solid foundation for students in important bioengineering concepts. Fully revised and updated to better meet the needs of instructors and students, the fourth edition expands on concepts introduced in the previous edition through computational methods that allow students to explore operations, such as correlations, convolution, the Fourier transform, and the transfer function. New medical examples and applications are included throughout the text. - Covers current applications in biocontrol, with examples from physiological systems modeling, such as the respiratory system - Features revised material throughout, with improved clarity of presentation and more biological, physiological, and medical examples and applications - Includes support materials, such as solutions, lecture slides, MATLAB data, and functions needed to solve problems |
| detrended fluctuation analysis example: Cardiac Electrophysiology: From Cell to Bedside E-Book Douglas P. Zipes, Jose Jalife, William Gregory Stevenson, 2017-05-13 Rapid advancements in cardiac electrophysiology require today’s health care scientists and practitioners to stay up to date with new information both at the bench and at the bedside. The fully revised 7th Edition of Cardiac Electrophysiology: From Cell to Bedside, by Drs. Douglas Zipes, Jose Jalife, and William Stevenson, provides the comprehensive, multidisciplinary coverage you need, including the underlying basic science and the latest clinical advances in the field. An attractive full-color design features color photos, tables, flow charts, ECGs, and more. All chapters have been significantly revised and updated by global leaders in the field, including 19 new chapters covering both basic and clinical topics. New topics include advances in basic science as well as recent clinical technology, such as leadless pacemakers; catheter ablation as a new class I recommendation for atrial fibrillation after failed medical therapy; current cardiac drugs and techniques; and a new video library covering topics that range from basic mapping (for the researcher) to clinical use (implantations). Each chapter is packed with the latest information necessary for optimal basic research as well as patient care, and additional figures, tables, and videos are readily available online. New editor William G. Stevenson, highly regarded in the EP community, brings a fresh perspective to this award-winning text. |
| detrended fluctuation analysis example: Scale-free Dynamics and Critical Phenomena in Cortical Activity Biyu J. He, Andreas Daffertshofer, Tjeerd W. Boonstra, The brain is composed of many interconnected neurons that form a complex system, from which thought, behavior, and creativity emerge through self-organization. By studying the dynamics of this network, some basic motifs can be identified. Recent technological and computational advances have led to rapidly accumulating empirical evidence that spontaneous cortical activity exhibits scale-free and critical behavior. Multiple experiments have identified neural processes without a preferred timescale in the avalanche-like spatial propagation of activity in cortical slices and in self-similar time series of local field potentials. Even at the largest scale, scale-free behavior can be observed by looking at the power distributions of brain rhythms as observed by neuroimaging. These findings may indicate that brain dynamics are always close to critical states – a fact with important consequences for how brain accomplishes information transfer and processing. Capitalizing on analogies between the collective behavior of interacting particles in complex physical systems and interacting neurons in the cortex, concepts from non-equilibrium thermodynamics can help to understand how dynamics are organized. In particular, the concepts of phase transitions and self-organized criticality can be used to shed new light on how to interpret collective neuronal dynamics. Despite converging support for scale-free and critical dynamics in cortical activity, the implications for accompanying cognitive functions are still largely unclear. This Research Topic aims to facilitate the discussion between scientists from different backgrounds, ranging from theoretical physics, to computational neuroscience, brain imaging and neurophysiology. By stimulating interactions with the readers of Frontiers in Physiology, we hope to advance our understanding of the role of scale-freeness and criticality in organizing brain dynamics. What do these new perspectives tell us about the brain and to what extent are they relevant for our cognitive functioning? For this Research Topic, we therefore solicit reviews, original research articles, opinion and method papers, which address the principles that organize the dynamics of cortical activity. While focusing on work in the neurosciences, this Research Topic also welcomes theoretical contributions from physics or computational approaches. |
| detrended fluctuation analysis example: Studies on Time Series Applications in Environmental Sciences Alina Bărbulescu, 2016-03-12 Time series analysis and modelling represent a large study field, implying the approach from the perspective of the time and frequency, with applications in different domains. Modelling hydro-meteorological time series is difficult due to the characteristics of these series, as long range dependence, spatial dependence, the correlation with other series. Continuous spatial data plays an important role in planning, risk assessment and decision making in environmental management. In this context, in this book we present various statistical tests and modelling techniques used for time series analysis, as well as applications to hydro-meteorological series from Dobrogea, a region situated in the south-eastern part of Romania, less studied till now. Part of the results are accompanied by their R code. |
| detrended fluctuation analysis example: Information Technology Applications in Industry Jun Zhang, Zhi Jian Wang, Shu Ren Zhu, Xiao Ming Meng, 2012-12-27 Selected, peer reviewed papers from the 2012 International Conference on Information Technology and Management Innovation (ICITMI 2012), November 10-11, 2012, Guangzhou, China |
| detrended fluctuation analysis example: Fractal and Multifractal Facets in the Structure and Dynamics of Physiological Systems and Applications to Homeostatic Control, Disease Diagnosis and Integrated Cyber-Physical Platforms Paul Bogdan, Plamen Ch. Ivanov, Andras Eke, 2020-06-25 Widespread chronic diseases (e.g., heart diseases, diabetes and its complications, stroke, cancer, brain diseases) constitute a significant cause of rising healthcare costs and pose a significant burden on quality-of-life for many individuals. Despite the increased need for smart healthcare sensing systems that monitor / measure patients’ body balance, there is no coherent theory that facilitates the modeling of human physiological processes and the design and optimization of future healthcare cyber-physical systems (HCPS). The HCPS are expected to mine the patient’s physiological state based on available continuous sensing, quantify risk indices corresponding to the onset of abnormality, signal the need for critical medical intervention in real-time by communicating patient’s medical information via a network from individual to hospital, and most importantly control (actuate) vital health signals (e.g., cardiac pacing, insulin level, blood pressure) within personalized homeostasis. To prevent health complications, maintain good health and/or avoid fatal conditions calls for a cross-disciplinary approach to HCPS design where recent statistical-physics inspired discoveries done by collaborations between physicists and physicians are shared and enriched by applied mathematicians, control theorists and bioengineers. This critical and urgent multi-disciplinary approach has to unify the current state of knowledge and address the following fundamental challenges: One fundamental challenge is represented by the need to mine and understand the complexity of the structure and dynamics of the physiological systems in healthy homeostasis and associated with a disease (such as diabetes). Along the same lines, we need rigorous mathematical techniques for identifying the interactions between integrated physiologic systems and understanding their role within the overall networking architecture of healthy dynamics. Another fundamental challenge calls for a deeper understanding of stochastic feedback and variability in biological systems and physiological processes, in particular, and for deciphering their implications not only on how to mathematically characterize homeostasis, but also on defining new control strategies that are accounting for intra- and inter-patient specificity – a truly mathematical approach to personalized medicine. Numerous recent studies have demonstrated that heart rate variability, blood glucose, neural signals and other interdependent physiological processes demonstrate fractal and non-stationary characteristics. Exploiting statistical physics concepts, numerous recent research studies demonstrated that healthy human physiological processes exhibit complex critical phenomena with deep implications for how homeostasis should be defined and how control strategies should be developed when prolonged abnormal deviations are observed. In addition, several efforts have tried to connect these fractal characteristics with new optimal control strategies that implemented in medical devices such as pacemakers and artificial pancreas could improve the efficiency of medical therapies and the quality-of-life of patients but neglecting the overall networking architecture of human physiology. Consequently, rigorously analyzing the complexity and dynamics of physiological processes (e.g., blood glucose and its associated implications and interdependencies with other physiological processes) represents a fundamental step towards providing a quantifiable (mathematical) definition of homeostasis in the context of critical phenomena, understanding the onset of chronic diseases, predicting deviations from healthy homeostasis and developing new more efficient medical therapies that carefully account for the physiological complexity, intra- and inter-patient variability, rather than ignoring it. This Research Topic aims to open a synergetic and timely effort between physicians, physicists, applied mathematicians, signal processing, bioengineering and biomedical experts to organize the state of knowledge in mining the complexity of physiological systems and their implications for constructing more accurate mathematical models and designing QoL-aware control strategies implemented in the new generation of HCPS devices. By bringing together multi-disciplinary researchers seeking to understand the many aspects of human physiology and its complexity, we aim at enabling a paradigm shift in designing future medical devices that translates mathematical characteristics in predictable mathematical models quantifying not only the degree of homeostasis, but also providing fundamentally new control strategies within the personalized medicine era. |
| detrended fluctuation analysis example: Quantitative Finance Maria Cristina Mariani, Ionut Florescu, 2019-11-06 Presents a multitude of topics relevant to the quantitative finance community by combining the best of the theory with the usefulness of applications Written by accomplished teachers and researchers in the field, this book presents quantitative finance theory through applications to specific practical problems and comes with accompanying coding techniques in R and MATLAB, and some generic pseudo-algorithms to modern finance. It also offers over 300 examples and exercises that are appropriate for the beginning student as well as the practitioner in the field. The Quantitative Finance book is divided into four parts. Part One begins by providing readers with the theoretical backdrop needed from probability and stochastic processes. We also present some useful finance concepts used throughout the book. In part two of the book we present the classical Black-Scholes-Merton model in a uniquely accessible and understandable way. Implied volatility as well as local volatility surfaces are also discussed. Next, solutions to Partial Differential Equations (PDE), wavelets and Fourier transforms are presented. Several methodologies for pricing options namely, tree methods, finite difference method and Monte Carlo simulation methods are also discussed. We conclude this part with a discussion on stochastic differential equations (SDE’s). In the third part of this book, several new and advanced models from current literature such as general Lvy processes, nonlinear PDE's for stochastic volatility models in a transaction fee market, PDE's in a jump-diffusion with stochastic volatility models and factor and copulas models are discussed. In part four of the book, we conclude with a solid presentation of the typical topics in fixed income securities and derivatives. We discuss models for pricing bonds market, marketable securities, credit default swaps (CDS) and securitizations. Classroom-tested over a three-year period with the input of students and experienced practitioners Emphasizes the volatility of financial analyses and interpretations Weaves theory with application throughout the book Utilizes R and MATLAB software programs Presents pseudo-algorithms for readers who do not have access to any particular programming system Supplemented with extensive author-maintained web site that includes helpful teaching hints, data sets, software programs, and additional content Quantitative Finance is an ideal textbook for upper-undergraduate and beginning graduate students in statistics, financial engineering, quantitative finance, and mathematical finance programs. It will also appeal to practitioners in the same fields. |
DFA: Detrended Fluctuation Analysis - The Comprehensive R …
Applies the log-amplitude Detrended Fluctuation Analysis (DFA) to nonstationary time series. The DFA log-amplitude fluctuation can be computed in a geometric scale or for different choices of …
Detrended fluctuation analysis as a regression framework: …
We propose a framework combining detrended fluctuation analysis with standard regression methodology. The method is built on detrended variances and covariances and it is designed …
Introduction to multifractal detrended fluctuation analysis in …
The present tutorial is an introduction to multifractal detrended fluctuation analysis (MFDFA) that estimates the multifractal spec-trum of biomedical time series.The tutorial presents MFDFA …
Low Scaling Exponent during Arrhythmia: Detrended …
Detrended fluctuation analysis (DFA) (Peng et al., 1995) was proposed as a potentially useful method for detecting the signs of cardiovascular disease (See Stanley et al., 1999); although …
DETRENDED FLUCTUATION ANALYSIS OF - arXiv.org
Detrended fluctuation analysis (DFA) was a method basically designed to investigate long range correlation in non stationary series [4-6]. DFA produces a fluctuation function F(n) as a function …
Advanced Methods in Detrended Fluctuation Analysis with …
Detrended fluctuation analysis (DFA) is a popular tool for studying these fractal scaling relations. Power laws become linear relationships in logarithmic scales, and conventionally these scaling …
Detrended Fluctuation Analysis (DFA) and R-R Interval
The detrended fluctuation analysis (DFA) [1] method is used to quantify the fractal-like scaling properties of the variability of cardiac parameters, i.e. R-R interval data. DFA has proved to be …
Using Continuous Glucose Monitoring Data and Detrended …
This paper presents a “how to” tutorial and review of work applying detrended fluctuation analysis to CGM signals. From the current published literature applying DFA to CGM signals it is …
DCCA: Detrended Fluctuation and Detrended Cross …
A collection of functions to perform Detrended Fluctuation Analysis (DFA) and Detrended Cross- Correlation Analysis (DCCA). This package implements the results presented in Prass, T.S. …
The fluctuation function of the detrended fluctuation analysis ...
fluctuation analysis proposes to obtain the correlation structure of a time series without knowing its structure. We want to illustrate this method in the following section.
Applications of Multifractal Detrended Fluctuation Anal- ysis
Abstract. In recent years, multifractal detrended fluctuation analysis (MF-DFA) has become an important tool for detecting the scale and long correlation of non-stationary time series. With …
fathon: A Python package for a fast computation of …
fathon is a Python package for DFA (detrended fluctuation analysis) and related algorithms. DFA (fathon.DFA) was first developed by Peng to study memory effects in sequences of DNA (Peng …
DETRENDED FLUCTUATION ANALYSIS FOR CONTINUOUS …
Based on the well-known Detrended Fluctuation Analysis (DFA) for time series, in this work we describe a DFA for continuous real variable functions. Under certain conditions, DFA …
Detrended Fluctuation Analysis on Cardiac Pulses in Both, …
The detrended fluctuation analysis (DFA) was developed by statistical physicists and has been reported as a useful method in the area of physiology and medicine. For example, it was …
Quantification of stress: a case study using modified …
Using this principle, we have reported that a method, modified detrended fluctuation analysis (mDFA), can distinguish between isolated hearts and intact hearts using the scaling exponent …
Nonlinear Analysis of Sleep Stages Using Detrended …
Detrended fluctuation analysis (DFA), which is suitable for non-stationary time series, is used to analyze the fluctuation of the EEG dynamics by calculating its scaling exponents.
Detecting Long-range Correlations with Detrended …
We examine the Detrended Fluctuation Analysis (DFA), which is a well-established method for the detection of long-range correlations in time series. We show that deviations from scaling that …
Fish Sound Characterization Using Multifractal Detrended …
The present work involves application of a nonlinear method, multifractal detrended fluctuation analysis (MFDFA) to describe fish sound data recorded from the open water of two major …
Comparison of detrending methods for fluctuation analysis
In the last decade Detrended Fluctuation Analysis (DFA), originally intro-duced by Peng et al. [1], has been established as an important method to reliably detect long-range (auto-) correlations1 …
Rescaled Range Analysis and Detrended Fluctuation Analysis: …
2.2 Detrended fluctuation analysis Detrended fluctuation analysis (DFA) was firstly proposed by Peng et al. (1994) while examining series of DNA nucleotides. Compared to the R/S …
DFA: Detrended Fluctuation Analysis - The Comprehensive …
Applies the log-amplitude Detrended Fluctuation Analysis (DFA) to nonstationary time series. The DFA log-amplitude fluctuation can be computed in a geometric scale or for different choices of …
Detrended fluctuation analysis as a regression framework: …
We propose a framework combining detrended fluctuation analysis with standard regression methodology. The method is built on detrended variances and covariances and it is designed …
Introduction to multifractal detrended fluctuation analysis …
The present tutorial is an introduction to multifractal detrended fluctuation analysis (MFDFA) that estimates the multifractal spec-trum of biomedical time series.The tutorial presents MFDFA …
Low Scaling Exponent during Arrhythmia: Detrended …
Detrended fluctuation analysis (DFA) (Peng et al., 1995) was proposed as a potentially useful method for detecting the signs of cardiovascular disease (See Stanley et al., 1999); although …
DETRENDED FLUCTUATION ANALYSIS OF - arXiv.org
Detrended fluctuation analysis (DFA) was a method basically designed to investigate long range correlation in non stationary series [4-6]. DFA produces a fluctuation function F(n) as a …
Advanced Methods in Detrended Fluctuation Analysis with …
Detrended fluctuation analysis (DFA) is a popular tool for studying these fractal scaling relations. Power laws become linear relationships in logarithmic scales, and conventionally these scaling …
Detrended Fluctuation Analysis (DFA) and R-R Interval
The detrended fluctuation analysis (DFA) [1] method is used to quantify the fractal-like scaling properties of the variability of cardiac parameters, i.e. R-R interval data. DFA has proved to be …
Using Continuous Glucose Monitoring Data and Detrended …
This paper presents a “how to” tutorial and review of work applying detrended fluctuation analysis to CGM signals. From the current published literature applying DFA to CGM signals it is …
DCCA: Detrended Fluctuation and Detrended Cross …
A collection of functions to perform Detrended Fluctuation Analysis (DFA) and Detrended Cross- Correlation Analysis (DCCA). This package implements the results presented in Prass, T.S. …
The fluctuation function of the detrended fluctuation …
fluctuation analysis proposes to obtain the correlation structure of a time series without knowing its structure. We want to illustrate this method in the following section.
Applications of Multifractal Detrended Fluctuation Anal- ysis
Abstract. In recent years, multifractal detrended fluctuation analysis (MF-DFA) has become an important tool for detecting the scale and long correlation of non-stationary time series. With …
fathon: A Python package for a fast computation of …
fathon is a Python package for DFA (detrended fluctuation analysis) and related algorithms. DFA (fathon.DFA) was first developed by Peng to study memory effects in sequences of DNA …
DETRENDED FLUCTUATION ANALYSIS FOR CONTINUOUS …
Based on the well-known Detrended Fluctuation Analysis (DFA) for time series, in this work we describe a DFA for continuous real variable functions. Under certain conditions, DFA …
Detrended Fluctuation Analysis on Cardiac Pulses in Both, …
The detrended fluctuation analysis (DFA) was developed by statistical physicists and has been reported as a useful method in the area of physiology and medicine. For example, it was …
Quantification of stress: a case study using modified …
Using this principle, we have reported that a method, modified detrended fluctuation analysis (mDFA), can distinguish between isolated hearts and intact hearts using the scaling exponent …
Nonlinear Analysis of Sleep Stages Using Detrended …
Detrended fluctuation analysis (DFA), which is suitable for non-stationary time series, is used to analyze the fluctuation of the EEG dynamics by calculating its scaling exponents.
Detecting Long-range Correlations with Detrended …
We examine the Detrended Fluctuation Analysis (DFA), which is a well-established method for the detection of long-range correlations in time series. We show that deviations from scaling that …
Fish Sound Characterization Using Multifractal Detrended …
The present work involves application of a nonlinear method, multifractal detrended fluctuation analysis (MFDFA) to describe fish sound data recorded from the open water of two major …
Comparison of detrending methods for fluctuation analysis
In the last decade Detrended Fluctuation Analysis (DFA), originally intro-duced by Peng et al. [1], has been established as an important method to reliably detect long-range (auto-) …
Rescaled Range Analysis and Detrended Fluctuation …
2.2 Detrended fluctuation analysis Detrended fluctuation analysis (DFA) was firstly proposed by Peng et al. (1994) while examining series of DNA nucleotides. Compared to the R/S …
