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.

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.

Multifractal detrended fluctuation analysis of temperature in Spain (1960–2019)

2021 ◽  
pp. 126118
Author(s):  
Javier Gómez-Gómez ◽  
Rafael Carmona-Cabezas ◽  
Ana B. Ariza-Villaverde ◽  
Eduardo Gutiérrez de Ravé ◽  
Francisco José Jiménez-Hornero
Keyword(s):  
Detrended Fluctuation Analysis ◽  
Fluctuation Analysis ◽  
Multifractal Detrended Fluctuation Analysis ◽  
Detrended Fluctuation
Sign in / Sign up

Export Citation Format

Share Document