Attractor Reconstruction for Chaotic Epileptic Signals

Healthcare Technology Letters, 2014

Temporal seizures due to hippocampal origins are very common among epileptic patients. Presented is a novel seizure prediction approach employing correlation and chaos theories. The early identification of seizure signature allows for various preventive measures to be undertaken. Electro-encephalography signals are spectrally broken down into the following sub-bands: delta; theta; alpha; beta; and gamma. The proposed approach consists of observing a high correlation level between any pair of electrodes for the lower frequencies and a decrease in the Lyapunov index (chaos or entropy) for the higher frequencies. Power spectral density and statistical analysis tools were used to determine threshold levels for the lower frequencies. After studying all five sub-bands, the analysis has revealed that the seizure signature can be extracted from the delta band and the high frequencies. High frequencies are defined as both the gamma band and the ripples occurring within the 60-120 Hz sub-band. To validate the proposed approach, six patients from both sexes and various age groups with temporal epilepsies originating from the hippocampal area were studied using the Freiburg database. An average seizure prediction of 30 min, an anticipation accuracy of 72%, and a false-positive rate of 0% were accomplished throughout 200 h of recording time.

International Journal of Computer and Electrical Engineering, 2012

An EMD-chaos based approach is proposed to discriminate EEG signals corresponding to healthy persons, and epileptic patients during seizure-free intervals and seizure attacks. An electroencephalogram (EEG) is first empirically decomposed to intrinsic mode functions (IMFs). The nonlinear dynamics of these IMFs are quantified in terms of the largest Lyapunov exponent (LLE) and correlation dimension (CD). This chaotic analysis in EMD domain is applied to a large group of EEG signals corresponding to healthy persons as well as epileptic patients (both with and without seizure attacks). It is shown that the values of the obtained LLE and CD exhibit features by which EEG for seizure attacks can be clearly distinguished from other EEG signals in the EMD domain. Thus, the proposed approach may aid researchers in developing effective techniques to predict seizure activities.

Basic and Clinical Neuroscience Journal, 2018

In this paper, nonlinear dynamical analysis based on Recurrence Quantification Analysis (RQA) is employed to characterize the nonlinear EEG dynamics. RQA can provide useful quantitative information on the regular, chaotic, or stochastic property of the underlying dynamics. Methods: We use the RQA-based measures as the quantitative features of the nonlinear EEG dynamics. Mutual Information (MI) was used to find the most relevant feature subset out of RQA-based features. The selected features were fed into an artificial neural network for grouping of EEG recordings to detect ictal, interictal, and healthy states. The performance of the proposed procedure was evaluated using a database for different classification cases. Results: The combination of five selected features based on MI achieved 100% accuracy, which demonstrates the superiority of the proposed method. Conclusion: The results showed that the nonlinear dynamical analysis based on Rcurrence Quantification Analysis (RQA) can be employed as a suitable approach for characterizing the nonlinear EEG dynamics and detecting the seizure.

2015

Electroencephalogram (EEG) signals carry information about the dynamics of the brain. A nonlinear method development is of great significance objective or goal because of the brain signals are nonlinear. Epilepsy is a neurological disorder which can be seen all over the world. It can be diagnosed by brain’s electrical activity. The determination of epileptic attacks or seizures by EEG signals is quite common in both clinical and research fields. During epileptic seizures, brain dynamics that make up the graph consists of abnormalities in EEG signals. Therefore, both time and frequency bands of the signals as well as need to go to review and determination of the pattern. Preictal and ictal EEG epochs were evaluated by wavelet-entropy and artificial neural networks (ANN) methods in this study. One hour EEG signals from different patients were used for wavelet-entropy method. One-hour epileptic EEG signals divided into two states (preictal and ictal) for this study. Then preictal end i...

2001

We address two aspects in chaotic time series analysis, namely the definition of embedding parameters and the largest Lyapunov exponent. It is necessary for performing state space reconstruction and identification of chaotic behavior. For the first aspect, we examine the mutual information for determination of time delay and false nearest neighbors method for choosing appropriate embedding dimension. For the second aspect we suggest neural network approach, which is characterized by simplicity and accuracy.

AIP Conference Proceedings, 2003

Comparison of the Nature of Chaos in Experimental [EEG] Data and Theoretical [ANN] Data | Browse -AIP Conference Proceedings In this paper, nonlinear dynamical tools like largest Lyapunov exponents (LE), fractal dimension, correlation dimension, pointwise correlation dimension will be employed to analyze electroencephalogram [EEG] data and determine the nature of chaos. Results of similar calculations from some earlier works will be produced for comparison with present results. Also, a brief report on difference of opinion among coworkers regarding tools to characterize chaos will be reported; particularly applicability of LE will be reviewed. The issue of nonlinearity present in experimental time series will be addressed by using surrogate data technique. We have extracted another data set which represented chaotic state of the system considered in our earlier work of mathematical modeling of artificial neural network. By comparing the values of measures employed to the two datasets, it can be concluded that EEG represents high dimensional chaos, whereas the experimental data due to its deterministic nature, is of low dimension. Also results give the evidence that LE exponent is applicable for low dimensional chaotic system while for experimental data, due to their stochasticity and presence of noise• LE is not a reliable tool to characterize chaos.

2003

It has been claimed that Lyapunov exponents computed from electroencephalogram or electrocorticogram (ECoG) time series are useful for early prediction of epileptic seizures. We show, by utilizing a paradigmatic chaotic system, that there are two major obstacles that can fundamentally hinder the predictive power of Lyapunov exponents computed from time series: finite-time statistical fluctuations and noise. A case study with an ECoG signal recorded from a patient with epilepsy is presented.

Physical Review E, 2007

We present an adaptive similarity-based approach to detect generalized synchronization ͑GS͒ with n : m phase synchronization ͑PS͒, where n and m are integers and one of them is 1. This approach is based on the similarity index ͑SI͒ and Gaussian mixture model with the minimum description length criterion. The clustering method, which is shown to be superior to the closeness and connectivity of a continuous function, is employed in this study to detect the existence of GS with n : m PS. We conducted a computer simulation and a finger-lifting experiment to illustrate the effectiveness of the proposed method. In the simulation of a Rössler-Lorenz system, our method outperformed the conventional SI, and GS with 2:1 PS within the coupled system was found. In the experiment of self-paced finger-lifting movement, cortico-muscular GS with 1:2 and 1:3 PS was found between the surface electromyogram signals on the first dorsal interossei muscle and the magnetoencephalographic data in the motor area. The GS with n : m PS ͑n or m =1͒ has been simultaneously resolved from both simulation and experiment. The proposed approach thereby provides a promising means for advancing research into both nonlinear dynamics and brain science.

13th Chaotic Modeling and Simulation International Conference, 2021

In this paper, we propose statistical methods and nonlinear dynamics for analyzing brain activity in epileptic patients, using the PhysioNet database. Thus, the analysis by statistical methods (the time variation of the standard deviation of the component signals of the electroencephalogram, the time variation of the signal variance, the time variation of the skewness, the time variation of the kurtosis, the construction of the recurrence maps corresponding to both normal functioning of the brain, as well as of the pre-crisis period, respectively of the crisis, the evolution in time of the spatial-temporal entropy, the variations of the Lyapunov coefficients, etc.) allows us to determine not only the epilepsy time based on a specific strange attractor but also that the entry into the epileptic seizure can be determined at least twenty minutes in advance. Finally, utilyzing elements of nonlinear dynamics and chaos, one builds in the states space certain attractors corresponding to a wide "class" of signals of encephalographic type. These classes dictate the normal or the abnormal

Introduction: In this paper, nonlinear dynamical analysis based on Recurrence Quantification Analysis (RQA) is employed to characterize the nonlinear EEG dynamics. RQA can provide useful quantitative information on the regular, chaotic, or stochastic property of the underlying dynamics.