A decision support framework for the discrimination of children with controlled epilepsy based on EEG analysis.

BACKGROUND: In this work we consider hidden signs (biomarkers) in ongoing EEG activity expressing epileptic tendency, for otherwise normal brain operation. More specifically, this study considers children with controlled epilepsy where only a few seizures without complications were noted before starting medication and who showed no clinical or electrophysiological signs of brain dysfunction. We compare EEG recordings from controlled epileptic children with age-matched control children under two different operations, an eyes closed rest condition and a mathematical task. The aim of this study is to develop reliable techniques for the extraction of biomarkers from EEG that indicate the presence of minor neurophysiological signs in cases where no clinical or significant EEG abnormalities are observed. METHODS: We compare two different approaches for localizing activity differences and retrieving relevant information for classifying the two groups. The first approach focuses on power spectrum analysis whereas the second approach analyzes the functional coupling of cortical assemblies using linear synchronization techniques. RESULTS: Differences could be detected during the control (rest) task, but not on the more demanding mathematical task. The spectral markers provide better diagnostic ability than their synchronization counterparts, even though a combination (or fusion) of both is needed for efficient classification of subjects. CONCLUSIONS: Based on these differences, the study proposes concrete biomarkers that can be used in a decision support system for clinical validation. Fusion of selected biomarkers in the Theta and Alpha bands resulted in an increase of the classification score up to 80% during the rest condition. No significant discrimination was achieved during the performance of a mathematical subtraction task.

The influence of ageing on complex brain networks: a graph theoretical analysis.

OBJECTIVE: To determine the functional connectivity of different EEG bands at the "baseline" situation (rest) and during mathematical thinking in children and young adults to study the maturation effect on brain networks at rest and during a cognitive task. METHODS: Twenty children (8-12 years) and twenty students (21-26 years) were studied. The synchronization likelihood was used to evaluate the interregional synchronization of different EEG frequency bands in children and adults, at rest and during math. Then, graphs were constructed and characterized in terms of local structure (clustering coefficient) and overall integration (path length) and the "optimal" organization of the connectivity i.e., the small world network (SWN). RESULTS: The main findings were: (i) Enhanced synchronization for theta band during math more prominent in adults. (ii) Decrease of the optimal SWN organization of the alpha2 band during math. (iii) The beta and especially gamma bands showed lower synchronization and signs of lower SWN organization in both situations in adults. CONCLUSION: There are interesting findings related to the two age groups and the two situations. The theta band showed higher synchronization during math in adults as a result of higher capacity of the working memory in this age group. The alpha2 band showed some SWN disorganization during math, a process analog to the known desynchronization. In adults, a dramatic reduction of the connections in gray matter occurs. Although this maturation process is probably related to higher efficiency, reduced connectivity is expressed by lower synchronization and lower mean values of the graph parameters in adults.

Detection of signs of brain dysfunction in epileptic children by recognition of transient changes in the correlation of seizure-free EEG.

Seizure-free EEG signals recorded from epileptic children were compared with EEG signals recorded from normal children. The comparison was based on the detection of transient events characterized by decrease in the correlation between different traces. For this purpose, a conceptually and mathematically simple method was applied. Two clear and remarkable phenomena, able to quantitatively discriminate between the two groups of subjects, were evidenced, with high statistical significance. In fact, it was observed that: (a) The number of events for the epileptic group was larger; (b) Applying restrictive criteria for event definition, the number of subjects in the epileptic group presenting events was larger. The results support the hypothesis of a decrease in brain correlation in children with epilepsy under treatment. This confirms the efficacy of the EEG signal in evaluating cortical functional differences not visible by visual inspection, independently of the cause (epilepsy or drugs), and demonstrate the specific effectiveness of the analysis method applied.

Simple and difficult mathematics in children: a minimum spanning tree EEG network analysis.

Sensor-level network characteristics associated with arithmetic tasks varying in complexity were estimated using tools from modern network theory. EEG signals from children with math difficulties (MD) and typically achieving controls (NI) were analyzed using minimum spanning tree (MST) indices derived from Phase Lag Index values - a graph method that corrects for comparison bias. Results demonstrated progressive modulation of certain MST parameters with increased task difficulty. These findings were consistent with more distributed network activation in the theta band, and greater network integration (i.e., tighter communication between involved regions) in the alpha band as task demands increased. There was also evidence of stronger intraregional signal inter-dependencies in the higher frequency bands during the complex math task. Although these findings did not differ between groups, several MST parameters were positively correlated with individual performance on psychometric math tasks involving similar operations, especially in the NI group. The findings support the potential utility of MST analyses to evaluate function-related electrocortical reactivity over a wide range of EEG frequencies in children.

Assessment of linear and nonlinear synchronization measures for analyzing EEG in a mild epileptic paradigm.

Epilepsy is one of the most common brain disorders and may result in brain dysfunction and cognitive disturbances. Epileptic seizures usually begin in childhood without being accommodated by brain damage and are tolerated by drugs that produce no brain dysfunction. In this study, cognitive function is evaluated in children with mild epileptic seizures controlled with common antiepileptic drugs. Under this prism, we propose a concise technical framework of combining and validating both linear and nonlinear methods to efficiently evaluate (in terms of synchronization) neurophysiological activity during a visual cognitive task consisting of fractal pattern observation. We investigate six measures of quantifying synchronous oscillatory activity based on different underlying assumptions. These measures include the coherence computed with the traditional formula and an alternative evaluation of it that relies on autoregressive models, an information theoretic measure known as minimum description length, a robust phase coupling measure known as phase-locking value, a reliable way of assessing generalized synchronization in state-space and an unbiased alternative called synchronization likelihood. Assessment is performed in three stages; initially, the nonlinear methods are validated on coupled nonlinear oscillators under increasing noise interference; second, surrogate data testing is performed to assess the possible nonlinear channel interdependencies of the acquired EEGs by comparing the synchronization indexes under the null hypothesis of stationary, linear dynamics; and finally, synchronization on the actual data is measured. The results on the actual data suggest that there is a significant difference between normal controls and epileptics, mostly apparent in occipital-parietal lobes during fractal observation tests.

Parametric and nonparametric EEG analysis for the evaluation of EEG activity in young children with controlled epilepsy.

There is an important evidence of differences in the EEG frequency spectrum of control subjects as compared to epileptic subjects. In particular, the study of children presents difficulties due to the early stages of brain development and the various forms of epilepsy indications. In this study, we consider children that developed epileptic crises in the past but without any other clinical, psychological, or visible neurophysiological findings. The aim of the paper is to develop reliable techniques for testing if such controlled epilepsy induces related spectral differences in the EEG. Spectral features extracted by using nonparametric, signal representation techniques (Fourier and wavelet transform) and a parametric, signal modeling technique (ARMA) are compared and their effect on the classification of the two groups is analyzed. The subjects performed two different tasks: a control (rest) task and a relatively difficult math task. The results show that spectral features extracted by modeling the EEG signals recorded from individual channels by an ARMA model give a higher discrimination between the two subject groups for the control task, where classification scores of up to 100% were obtained with a linear discriminant classifier.