Nonlinear Characteristics Identification And Analysis For Epileptic Eeg Signals

Since the 1980s, new measures of epileptic EEG based on discipline of nonlinear dynamical systems (chaos) have been developed in the last decade. However chaos-based approaches need to assume that EEG data possesses a non-evolving low-dimensional attractor, and requires a long, stationary and noiseless EEG data to compute the reconstructed attractor's properties. To overcome the drawbacks of traditional nonlinear methods and meet the requirement of epileptic EEG analysis, this dissertation develops new methods to characterize EEG changes in different epileptic seizure phases.Firstly, the hybrid recurrence plot (HRP), based on traditional recurrence plot (RP) and order recurrence plot (ORP), is proposed to analyze the absence EEG. The innovation of HRP is that the recurrence is defined not only by local phase space distance, but also by the local order patterns structure of a time series. The simulation results demonstrate that the determinism measure DET, based on the diagonal structure of HRP, can reveal the changes of model parameters; and the HRP method is much more robust against noise than the traditional RP and ORP methods. Furthermore, the HRP is applied to indicate the deterministic dynamics of EEG recordings at the seizure-free, pre-seizure and seizure states in genetic absence epilepsy rat. It is found that the DET values of pre-seizure EEG data are significantly higher than those of seizure-free intervals, but lower than those of seizure intervals. These results demonstrate that EEG epochs during pre-seizure intervals exhibit a higher degree of determinism than seizure-free EEG epochs, but lower than those in seizure EEG epochs in absence epilepsy rat.Secondly, in order to further investigate hidden nonlinear dynamic characteristics in EEG data for differentiating absence seizure phases, this dissertation proposes a novel dissimilarity measure based on the ordinal pattern distributions of EEG recordings. The dissimilarity between two EEG epochs can be qualified via a simple distance measure between the distributions of order patterns. A neural mass model is proposed to simulate EEG data and to valid the performance of the dissimilarity measure. Furthermore, the proposed dissimilarity measure is applied to analyze absence EEG data with moving-window technique, which show that this measure successfully detects pre-seizure phases prior to their onset in 58 out of 110 seizures. This suggested that the dissimilarity measure could be used to detect changes in the dynamics of absence EEG data.Thirdly, because the synchronization analysis of EEG recordings has a great role for the study of epileptic seizures, a novel method, based on order pattern analysis, is proposed to estimate the mutual information between EEGs. The proposed method calculates the probability distribution of time series based on the classification of order patterns to directly estimate the amount of information in time series. The coupled Henon map model and coupled neural mass model are proposed to generate coupled time series and to valid the performance of the proposed mutual information method. Compared to traditional histogram method, this proposed method is better for presenting the change of the coupling coefficient in coupled models, and is more robust to noise. Furthermore, the proposed mutual information estimation is applied to analyze epileptic EEG data, which show that mutual information between EEGs gradually increases until it reaches its maximum at full seizure.Finally, permutation auto mutual information (AMI) is proposed as a tool to evaluate the dynamic characteristics of EEG during seizure-free, pre-seizure and seizure phase, respectively. Simulation results show that the AMI rate of decrease with increasing delay shiftedδis different between noise and chaotic series, which demonstrates that AMI method could be able to distinguish between these two different series. Using this proposed method to analyze epileptic EEG data, the results show that the AMI(δ) gradually decrease and firstly reach to constant value about atδ=5-6,δ=7-8 andδ=9-10 in seizure-free, pre-seizure and seizure phase, respectively. Furthermore, combining the LDA classifier, the results confirm that the AMI method has potential in classifying the epileptic EEG recordings.