scholarly journals Wavelet Analysis Applied on EEG Signals for Identification of Preictal States in Epileptic Patients

2020 ◽  
Vol 4 (3) ◽  
pp. 1730-1747
Author(s):  
Jade Barbosa Kill ◽  
Patrick Marques Ciarelli ◽  
Klaus Fabian Côco ◽  
Mariane Lima Souza
Keyword(s):  
Wavelet Analysis ◽  
Eeg Signals ◽  
Epileptic Patients

Wavelet analysis as a tool for investigating movement-related cortical oscillations in EEG signals acquired during 3T fMRI recordings

NeuroImage ◽  
2009 ◽  
Vol 47 ◽  
pp. S170
Author(s):  
S.F. Storti ◽  
E. Formaggio ◽  
M. Avesani ◽  
M. Acler ◽  
F. Alessandrini ◽  
...  
Keyword(s):  
Wavelet Analysis ◽  
Eeg Signals ◽  
Cortical Oscillations

scholarly journals A study on wavelet analysis of SSVEP Signals

2018 ◽  
Vol 7 (2.25) ◽  
pp. 10
Author(s):  
Bincy Babu ◽  
R Chandrasekaran ◽  
Josline Elsa Joseph ◽  
Thella Shalem Rahul ◽  
T R Thamizhvani ◽  
...  
Keyword(s):  
Wavelet Analysis ◽  
Visual Evoked Potential ◽  
Evoked Potential ◽  
Spectral Estimation ◽  
Power Spectral Analysis ◽  
Feature Analysis ◽  
Eeg Signals ◽  
Power Spectral ◽  
Frequency Components ◽  
Bci System

Almost every Brain Control Interfcae (BCI) system is framed based on Steady State Visual Evoked Potential (SSVEP) which is predicted through distinguishing overriding frequency components in Electroencephalography (EEG) signals. The proposed system aims in accurate feature extraction of SSVEP signals. Power spectral analysis and wavelet analysis are done for feature analysis. The feature set variation for male and female subjects are obtained. Compared power spectral estimation and wavelet analysis, merits and demerits of each approach can be identified from the outcomes. It offers a theoretical reference of practical choice for BCI application.  

EEG signals classification of epileptic patients via feature selection and voting criteria in intelligent method

2014 ◽  
Vol 38 (3) ◽  
pp. 146-155 ◽  
Author(s):  
Ali Ghaffari ◽  
H. Ebrahimi Orimi
Keyword(s):  
Feature Selection ◽  
Eeg Signals ◽  
Epileptic Patients

scholarly journals Data Mining Based Approach for Evaluation of EEG Signals for Epilepsy Detection

2020 ◽  
Vol 17 ◽  
Keyword(s):  
Data Mining ◽  
Time Series ◽  
Physiological Signals ◽  
Alternative Formulation ◽  
Eeg Signal ◽  
Detection Problem ◽  
Eeg Signals ◽  
Epileptic Patients ◽  
To Come

The objective of this proposed research is to come up with a general methodology for classification of time series events, and to apply that methodology to the analysis of physiological signals recorded from epileptic patients for seizure analysis depending on EEG signal. In contrast to previous works, this research considered an alternative formulation of seizure analysis as a detection problem. This approach offers a good treatment of seizure detection

scholarly journals Unstable periodic orbits are faithful biomarker for the onset of epileptic seizure

2021 ◽  
Author(s):  
Mayukha Pal ◽  
Sree Bhattacherjee ◽  
Prasanta K. Panigrahi
Keyword(s):  
Periodic Orbits ◽  
Epileptic Seizure ◽  
Power Spectra ◽  
Rate Of Change ◽  
Unstable Periodic Orbits ◽  
Healthy Individuals ◽  
Eeg Signals ◽  
Higher Harmonic Generation ◽  
Epileptic Patients ◽  
Higher Harmonic

AbstractEEG signals of healthy individuals and epileptic patients, when treated as time series of evolving dynamical systems, are found to display characteristic differences in the behavior of the unstable periodic orbits (UPO), marking the transition from regular periodic variations to self-similar dynamics. The UPO, manifesting as broad resonances in the Fourier power spectra, are quite prominent in their presence in the normal signals and are either absent or considerably weakened with a shift towards lower frequency in the epileptic condition. The weighted average and visibility power computed for the UPO region are found to distinguish epileptic seizure from healthy individuals’ EEG. Remarkably, the unstable periodic motion for healthy ones is well described by damped harmonic motion, the orbits displaying smooth dynamics. In contrast, the epileptic cases show bi-stability and piecewise linear motion for the larger orbits, exhibiting large sudden jumps in the ‘velocity’ (referred to the rate of change of the EEG potentials), characteristically different from the healthy cases, highlighting the efficacy of the UPO as biomarkers. For both the regions, 8-14Hz UPO and 40-45Hz resonance, we used data driven analysis to derive the system dynamics in terms of sinusoidal functions, which reveal the presence of higher harmonics, confirming nonlinearity of the underlying system and leading to quantification of the discernible differences between the healthy and epileptic patients. The gamma wave region in the 40-45Hz range, connecting the conscious and the unconscious states of the brain, reveals well-structured coherence phenomena, in addition to the prominent resonance, which potentially can be used as a biomarker for the epileptic seizure. The wavelet scalogram analysis for both UPO and 40-45Hz region also clearly differentiates the healthy condition from epileptic seizure, confirming the above dynamical picture, depicting the higher harmonic generation, and intermixing of different modes in these two regions of interest.SignificanceUnstable periodic orbits are demonstrated as faithful biomarkers for detecting seizure, being prominently present in the Fourier power spectra of the EEG signals of the healthy individuals and either being absent or significantly suppressed for the epileptic cases, showing distinctly different behavior for the unstable orbits, in the two cases. A phase space study, with EEG potential and its rate of change as coordinate and corresponding velocity, clearly delineates the dynamics in healthy and diseased individuals, demonstrating the absence or weakening of UPO, that can be a reliable bio-signature for the epileptic seizure. The phase-space analysis in the gamma region also shows specific signatures in the form of coherent oscillations and higher harmonic generation, further confirmed through wavelet analysis.

Detection of crossover points in detrended fluctuation analysis: An application to EEG signals of patients with epilepsy

2020 ◽  
Author(s):  
Victor Barreto Mesquita ◽  
Florêncio Mendes Oliveira Filho ◽  
Paulo Canas Rodrigues
Keyword(s):  
Long Range ◽  
Brain Activity ◽  
Epileptic Seizures ◽  
Research Direction ◽  
R Package ◽  
Supplementary Information ◽  
Fluctuation Analysis ◽  
Eeg Signals ◽  
Amplitude Function ◽  
Epileptic Patients

Abstract Motivation The quantification of long-range correlation of electroencephalogram (EEG) signals is an important research direction for its relevance in helping understanding the brain activity. Epileptic seizures have been studied in the past years where different non-linear statistical approaches have been employed to understand the relationship between the EEG signal and the epilepitc discharge. One of the most widely used method for to analyse long memory processes is the detrended fuctuation analysis (DFA). However, no objective and pragmatic methods have been developed to detect crossover points and reference channels in DFA. Results In this paper, we propose: (i) two automatic approaches that successfully detect crossover points in DFA related methods on the log-log plot; and (ii) a criteria to choose the reference channel for the log-amplitude function. Moreover, the DFA is applied to EEG signals of 10 epileptic patients collected from the CHB-MIT database, being the log-amplitude function used to compare the different brain hemispheres by making use of the methodology proposed in the paper. The existence of long-range power-law correlations is demonstrated and indicates that the EEG signals of epileptic patients present three well defined regions with the first region showing a 1/f 1/f noise (pink noise) for seven subjects and a random walk behaviour for three subjects. The second and third regions show anti-persistence behaviour. Moreover, the results of the log-amplitude function were divided in two groups: (i) the first, including seven subjects, where the increase in the scales results in an increase in the fluctuation in the the frontal channels; and (ii) the second, included three subjects, where the fluctuation for large scales are greater for the parietal channels. Availability The functions used in this paper are available in the R package DFA (Mesquita et al., 2020). Supplementary information Supplementary information are available at Bioinformatics online.

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