Mapping the epileptic brain with EEG dynamical connectivity: established methods and novel approaches

Several algorithms rooted in statistical physics, mathematics and machine learning are used to analyze neuroimaging data from patients suffering from epilepsy, with the main goals of localizing the brain region where the seizure originates from and of detecting upcoming seizure activity in order to trigger therapeutic neurostimulation devices. Some of these methods explore the dynamical connections between brain regions, exploiting the high temporal resolution of the electroencephalographic signals recorded at the scalp or directly from the cortical surface or in deeper brain areas. In this paper we describe this specific class of algorithms and their clinical application, by reviewing the state of the art and reporting their application on EEG data from an epileptic patient

Detection and Prediction of Absence Seizures Based on Nonlinear Analysis of the EEG in Wag/Rij Animal Model

 Background: Epilepsy is a common neurological disorder with a prevalence of 1% of the world population. Absence epilepsy is a form of generalized seizures with Spike wave discharge in EEG. Epileptic patients have frequent absence seizures that cause immediate loss of consciousness.Methods: In this study, it has been tried to explore whether EEG changes can effectively detect epilepsy in animal model applying non-linear features. To predict the occurrence of absence epilepsy, a long-term EEG signal has been recorded from frontal cortex in seven Wag/Rij rats. After preprocessing, the data was transferred to the phase space to extract the brain system dynamic and geometric properties of this space. Finally, the ability of each features to predict and detect absence epilepsy with two criteria of predictive time and the accuracy of detection and its results were compared with previous studies.Results: The results indicate that the brain system dynamic changes during the transition from free-seizure to pre-seizure and then seizure. Proposed approach diagnostic characteristics yielded 97% accuracy of absence epilepsy diagnosis indicating that due to the nonlinear and complex nature of the system and the brain signal, the use of methods consistent with this nature is important in understanding the dynamic transfer between different epileptic seizures.Conclusion: By changing the state of the absence Seizures, the dynamics are changing, and the results of this research can be useful in real-time applications such as predicting epileptic seizures

Automatic epilepsy detection using fractal dimensions segmentation and GP-SVM classification

Objective: The most important part of signal processing for classification is feature extraction as a mapping from original input electroencephalographic (EEG) data space to new features space with the biggest class separability value. Features are not only the most important, but also the most difficult task from the classification process as they define input data and classification quality. An ideal set of features would make the classification problem trivial. This article presents novel methods of feature extraction processing and automatic epilepsy seizure classification combining machine learning methods with genetic evolution algorithms. Methods: Classification is performed on EEG data that represent electric brain activity. At first, the signal is preprocessed with digital filtration and adaptive segmentation using fractal dimensions as the only segmentation measure. In the next step, a novel method using genetic programming (GP) combined with support vector machine (SVM) confusion matrix as fitness function weight is used to extract feature vectors compressed into lower dimension space and classify the final result into ictal or interictal epochs. Results: The final application of GP SVM method improves the discriminatory performance of a classifier by reducing feature dimensionality at the same time. Members of the GP tree structure represent the features themselves and their number is automatically decided by the compression function introduced in this paper. This novel method improves the overall performance of the SVM classification by dramatically reducing the size of input feature vector. Conclusion: According to results, the accuracy of this algorithm is very high and comparable, or even superior to other automatic detection algorithms. In combination with the great efficiency, this algorithm can be used in real-time epilepsy detection applications. From the results of the algorithm's classification, we can observe high sensitivity, specificity results, except for the Generalized Tonic Clonic Seizure (GTCS). As the next step, the optimization of the compression stage and final SVM evaluation stage is in place. More data need to be obtained on GTCS to improve the overall classification score for GTCS.Web of Science142449243

Range entropy: A bridge between signal complexity and self-similarity

Approximate entropy (ApEn) and sample entropy (SampEn) are widely used for temporal complexity analysis of real-world phenomena. However, their relationship with the Hurst exponent as a measure of self-similarity is not widely studied. Additionally, ApEn and SampEn are susceptible to signal amplitude changes. A common practice for addressing this issue is to correct their input signal amplitude by its standard deviation. In this study, we first show, using simulations, that ApEn and SampEn are related to the Hurst exponent in their tolerance r and embedding dimension m parameters. We then propose a modification to ApEn and SampEn called range entropy or RangeEn. We show that RangeEn is more robust to nonstationary signal changes, and it has a more linear relationship with the Hurst exponent, compared to ApEn and SampEn. RangeEn is bounded in the tolerance r-plane between 0 (maximum entropy) and 1 (minimum entropy) and it has no need for signal amplitude correction. Finally, we demonstrate the clinical usefulness of signal entropy measures for characterisation of epileptic EEG data as a real-world example.Comment: This is the revised and published version in Entrop