scholarly journals Investigating EEG Signals of Autistic Individuals Using Detrended Fluctuation Analysis

2021 ◽  
Vol 38 (5) ◽  
pp. 1515-1520
Author(s):  
Menaka Radhakrishnan ◽  
Karthik Ramamurthy ◽  
Avantika Kothandaraman ◽  
Gauri Madaan ◽  
Harini Machavaram
Keyword(s):  
Detrended Fluctuation Analysis ◽  
Autism Spectrum ◽  
Brain Diseases ◽  
Fluctuation Analysis ◽  
Typically Developing ◽  
Eeg Signals ◽  
Self Similarity ◽  
Detrended Fluctuation ◽  
Electroencephalogram Eeg ◽  
The Brain

To record all electrical activity of the human brain, an electroencephalogram (EEG) test using electrodes attached to the scalp is conducted. Analysis of EEG signals plays an important role in the diagnosis and treatment of brain diseases in the biomedical field. One of the brain diseases found in early ages include autism. Autistic behaviours are hard to distinguish, varying from mild impairments, to intensive interruption in daily life. The non-linear EEG signals arising from various lobes of the brain have been studied with the help of a robust technique called Detrended Fluctuation Analysis (DFA). Here, we study the EEG signals of Typically Developing (TD) and children with Autism Spectrum Disorder (ASD) using DFA. The Hurst exponents, which are the outputs of DFA, are used to find out the strength of self-similarity in the signals. Our analysis works towards analysing if DFA can be a helpful analysis for the early detection of ASD.

Identification of Chaos-Periodic Transitions, Band Merging, and Internal Crisis Using Wavelet-DFA Method

2016 ◽  
Vol 26 (04) ◽  
pp. 1650065 ◽  
Author(s):  
Mahsa Vaghefi ◽  
Ali Motie Nasrabadi ◽  
Seyed Mohammad Reza Hashemi Golpayegani ◽  
Mohammad Reza Mohammadi ◽  
Shahriar Gharibzadeh
Keyword(s):  
Detrended Fluctuation Analysis ◽  
Period Doubling ◽  
Nonstationary Time Series ◽  
The Self ◽  
Fluctuation Analysis ◽  
Scaling Analysis ◽  
Analysis Method ◽  
Self Similarity ◽  
Self Similar ◽  
Detrended Fluctuation

Detrended Fluctuation Analysis (DFA) is a scaling analysis method that can identify intrinsic self-similarity in any nonstationary time series. In contrast, Wavelet Transform (WT) method is widely used to investigate the self-similar processes, as the self-similarity properties exist within the subbands. Therefore, a combination of these two approaches, DFA and WPT, is promising for rigorous investigation of such a system. In this paper a new methodology, so-called wavelet DFA, is introduced and interpreted to evaluate this idea. This approach, further than identifying self-similarity properties, enable us to detect and capture the chaos-periodic transitions, band merging, and internal crisis in systems that become chaotic through period-doubling phenomena. Changes of wavelet DFA exponent have been compared with that of Lyapunov and DFA through Logistic, Sine, Gaussian, Cubic, and Quartic Maps. Furthermore, the potential capabilities of this new exponent have been presented.

Neurodynamical Control of the Heart of Healthy and Dying Crustacean Animals

2005 ◽  
Author(s):  
Toru Yazawa ◽  
Katsunori Tanaka ◽  
Tomoo Katsuyama
Keyword(s):  
Detrended Fluctuation Analysis ◽  
Terminal Condition ◽  
Fluctuation Analysis ◽  
Control Network ◽  
Cardiac Control ◽  
Freely Moving ◽  
Detrended Fluctuation ◽  
The Brain ◽  
Delayed Time ◽  
Embedding Methods

We analyzed the heartbeat-interval recorded from crustacean animals, using detrended fluctuation analysis (DFA) and delayed-time embedding method. EKG was obtained from freely moving animals in normal condition and then in terminal condition; we kept recording until the life was coming to an end. Our experimental purpose was to know whether DFA and embedding methods characterize quantitatively conditions of the cardiac control network, either in the brain or in the heart, or both, the brain and heart. We concluded that DFA exponents represent whether the subjects are under sick or healthy conditions. Here we show how the controller conditions of the brain changed and how pacemaker neural network in the heart deteriorated from time to time. This report demonstrates relationship between DFA and electro-physiological of the heart.

Scaling analysis of heart beat fluctuations data and its relationship with cyclic alternating pattern data during sleep

2016 ◽  
Vol 27 (07) ◽  
pp. 1650071 ◽  
Author(s):  
R. De León-Lomelí ◽  
J. S. Murguía ◽  
I. Chouvarda ◽  
M. O. Méndez ◽  
E. González-Galván ◽  
...  
Keyword(s):  
Detrended Fluctuation Analysis ◽  
Heart Beat ◽  
Fluctuation Analysis ◽  
Scaling Analysis ◽  
Dynamic Phenomenon ◽  
Scaling Method ◽  
Cyclic Alternating Pattern ◽  
Temporal Influence ◽  
Detrended Fluctuation ◽  
The Brain

During sleep there exists a nonlinear dynamic phenomenon, which is called cyclic alternating pattern. This phenomenon is generated in the brain and is composed of a series of events of short duration known as A-phases. It has been shown that A-phases can be found in other physiological systems such as the cardiovascular. However, there is no evidence that shows the temporal influence of the A-phases with the cardiovascular system. For this purpose, we consider the scaling method known as detrended fluctuation analysis (DFA). The analysis was carried out in well sleepers and insomnia people, and the numerical results show an increment in the scaling parameter for the insomnia subjects compared with the normal ones. In addition, the results of the heart dynamics suggests a persistent behavior toward the [Formula: see text]-noise.

THE EFFICIENCY OF SIN STOCKS: A MULTIFRACTAL ANALYSIS OF DRUG INDICES

2021 ◽  
pp. 1-22
Author(s):  
Faheem Aslam ◽  
Paulo Ferreira ◽  
Fahd Amjad ◽  
Haider Ali
Keyword(s):  
Time Series ◽  
Market Efficiency ◽  
Detrended Fluctuation Analysis ◽  
Multifractal Analysis ◽  
Fluctuation Analysis ◽  
Self Similarity ◽  
Multifractal Detrended Fluctuation Analysis ◽  
The North ◽  
Daily Data ◽  
Detrended Fluctuation

This study provides the first evidence of market efficiency of drug indices, especially cannabis and tobacco, which are known in finance as sin markets. The multifractal detrended fluctuation analysis (MFDFA) is employed on the daily data of six cannabis and one tobacco indices in order to measure efficiency by quantifying the intensity of self-similarity. The findings confirm multifractality in all sample series. Interestingly, Dow Jones Tobacco (DJUSTB) Index shows the highest multifractality, demonstrating the lowest efficiency, whereas S&P/TSX Cannabis (SPTXCAN) Index is the most efficient of all the time series under analysis, with the lowest multifractality levels. Only the North American Marijuana (NAMMAR), Cannabis World Index Gross Total Return (CANWLDGR) and DJUSTB show persistent behavior. These findings could be of interest to policymakers and regulators to establish new reforms to improve the efficiency of these markets, as well as for actual and potential investors.

scholarly journals Detrended Fluctuation Analysis of EEG Signals

2014 ◽  
Vol 12 ◽  
pp. 125-132 ◽  
Author(s):  
L.F. Márton ◽  
S.T. Brassai ◽  
L. Bakó ◽  
L. Losonczi
Keyword(s):  
Detrended Fluctuation Analysis ◽  
Fluctuation Analysis ◽  
Eeg Signals ◽  
Detrended Fluctuation
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