Comparative Analysis of Non-linear Behaviour with Power Spectral Intensity Response Between Normal and Epileptic EEG Signals

2017 ◽  
Author(s):  
Budhaditya Ghosh ◽  
Sourya Sengupta ◽  
Sayan Nag ◽  
Sayan Biswas ◽  
Shankha Sanyal
Keyword(s):  
Detrended Fluctuation Analysis ◽  
Nerve Cells ◽  
Spectral Intensity ◽  
Fluctuation Analysis ◽  
Eeg Signal ◽  
Frequency Bands ◽  
Power Spectral ◽  
Eeg Data ◽  
Detrended Fluctuation ◽  
The Brain

Epilepsy is a neurological condition which affects the nervous system. It is a general term used for a group of disorders in which nerve cells of the brain discharge anomalous electrical impulses from time to time, causing a temporary malfunction of the other nerve cells of the brain. EEG signal provides an important cue for diagnosis and interpretation related to prognosis of epilepsy. In this work we envisage to provide novel tool which can be used to detect the prognosis of epileptic disorder by comparing linear and nonlinear modalities of EEG analysis conventionally used Power spectral analysis and a robust non linear method, Detrended Fluctuation Analysis (DFA). Publicly available dataset is used for this work consisting of 100 normal patients EEG data as control group and 100 epileptic patients EEG data for comparison. Response for different frequency bands (alpha, theta, beta) of the EEG spectrum have been analyzed using Detrended Fluctuation Analysis (DFA) and Power Spectral Intensity (PSI). The comparison of the DFA scaling exponent with the spectral power data is calculated for all the 3 different frequency bands of EEG signal provide new and interesting results which have been discussed in detail.

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.

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.

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.

scholarly journals NREM Sleep EEG Characteristics Correlate to the Mild Cognitive Impairment in Patients with Parkinsonism

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Cheng Zhang ◽  
Luhua Wei ◽  
Fengqingyang Zeng ◽  
Tingwei Zhang ◽  
Yunchuang Sun ◽  
...  
Keyword(s):  
Spectral Analysis ◽  
Cognitive Impairment ◽  
Detrended Fluctuation Analysis ◽  
Power Spectral Analysis ◽  
Nrem Sleep ◽  
Sleep Eeg ◽  
Fluctuation Analysis ◽  
Eeg Activity ◽  
Power Spectral ◽  
Detrended Fluctuation

Early identification and diagnosis of mild cognitive impairment (MCI) in patients with parkinsonism (PDS) are critical. The aim of this study was to identify biomarkers of MCI in PDS using conventional electroencephalogram (EEG) power spectral analysis and detrended fluctuation analysis (DFA). In this retrospective study, patients with PDS who underwent an overnight polysomnography (PSG) study in our hospital from 2019 to 2020 were enrolled. Patients with PDS assessed by clinical examination and questionnaires were divided into two groups: the PDS with normal cognitive function (PDS-NC) group and the PDS with MCI (PDS-MCI) group. Sleep EEG signals were extracted and purified from the PSG and subjected to a conventional power spectral analysis, as well as detrended fluctuation analysis (DFA) during wakefulness, nonrapid eye movement (NREM) sleep, and rapid eye movement (REM) sleep. Forty patients with PDS were enrolled, including 25 with PDS-NC and 15 with PDS-MCI. Results revealed that compared with PDS-NC patients, patients with PDS-MCI had a reduced fast ratio ( alpha + beta / delta + theta ) and increased DFA during NREM sleep. DFA during NREM was diagnostic of PDS-MCI, with an area under the receiver operating characteristic curve of 0.753 (95% CI: 0.592–0.914) ( p < 0.05 ). Mild cognitive dysfunction was positively correlated with NREM-DFA ( r = 0.426 , p = 0.007 ) and negatively correlated with an NREM-fast ratio ( r = − 0.524 , p = 0.001 ). This suggested that altered EEG activity during NREM sleep is associated with MCI in patients with PDS. NREM sleep EEG characteristics of the power spectral analysis and DFA correlate to MCI. Slowing of EEG activity during NREM sleep may reflect contribution to the decline in NREM physiological function and is therefore a marker in patients with PDS-MCI.

scholarly journals Biases in the Simulation and Analysis of Fractal Processes

2019 ◽  
Vol 2019 ◽  
pp. 1-12 ◽  
Author(s):  
Clément Roume ◽  
Samar Ezzina ◽  
Hubert Blain ◽  
Didier Delignières
Keyword(s):  
Detrended Fluctuation Analysis ◽  
Cross Validation ◽  
Spectral Synthesis ◽  
Synthesis Method ◽  
Simulation Method ◽  
Fluctuation Analysis ◽  
Methods Of Analysis ◽  
Power Spectral ◽  
Systematic Biases ◽  
Detrended Fluctuation

Fractal processes have recently received a growing interest, especially in the domain of rehabilitation. More precisely, the evolution of fractality with aging and disease, suggesting a loss of complexity, has inspired a number of studies that tried, for example, to entrain patients with fractal rhythms. This kind of study requires relevant methods for generating fractal signals and for assessing the fractality of the series produced by participants. In the present work, we engaged a cross validation of three methods of generation and three methods of analysis. We generated exact fractal series with the Davies–Harte (DH) algorithm, the spectral synthesis method (SSM), and the ARFIMA simulation method. The series were analyzed by detrended fluctuation analysis (DFA), power spectral density (PSD) method, and ARFIMA modeling. Results show that some methods of generation present systematic biases: DH presented a strong bias toward white noise in fBm series close to the 1/f boundary and SSM produced series with a larger variability around the expected exponent, as compared with other methods. In contrast, ARFIMA simulations provided quite accurate series, without major bias. Concerning the methods of analysis, DFA tended to systematically underestimate fBm series. In contrast, PSD yielded overestimates for fBm series. With DFA, the variability of estimates tended to increase for fGn series as they approached the 1/f boundary and reached unacceptable levels for fBm series. The highest levels of variability were produced by PSD. Finally, ARFIMA methods generated the best series and provided the most accurate and less variable estimates.

Detrended Fluctuation Analysis Features for Automated Sleep Staging of Sleep EEG

2012 ◽  
Vol 1 (4) ◽  
pp. 47-59 ◽  
Author(s):  
Amr F. Farag ◽  
Shereen M. El-Metwally
Keyword(s):  
Eye Movement ◽  
Power Law ◽  
Detrended Fluctuation Analysis ◽  
Sleep Eeg ◽  
Sleep Stages ◽  
Fluctuation Analysis ◽  
Eeg Signal ◽  
Rapid Eye Movement ◽  
Sleep Staging ◽  
Detrended Fluctuation

An accurate sleep staging is crucial for the treatment of sleep disorders. Recently some studies demonstrated that the long range correlations of many physiological signals measured during sleep show some variations during the different sleep stages. In this study, detrended fluctuation analysis (DFA) is used to study the electroencephalogram (EEG) signal autocorrelation during different sleep stages. A classification of these stages is then made by introducing the calculated DFA power law exponents to a K-Nearest Neighbor classifier. The authors’ study reveals that a 2-D feature space composed of the DFA power law exponents of both the filtered THETA and BETA brain waves resulted in a classification accuracy of 93.52%, 93.52%, and 92.59% for the wake, non-rapid eye movement and rapid eye movement stages, respectively. The overall accuracy of the proposed system is 93.21%. The authors conclude that it might be possible to build an automated sleep assessment system based on DFA analysis of the sleep EEG signal.

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