Decreased Synchronization in Alcoholics Using EEG

An Eigenvalues-Based Covariance Matrix Bootstrap Model Integrated With Support Vector Machines for Multichannel EEG Signals Analysis

Identification of alcoholism is clinically important because of the way it affects the operation of the brain. Alcoholics are more vulnerable to health issues, such as immune disorders, high blood pressure, brain anomalies, and heart problems. These health issues are also a significant cost to national health systems. To help health professionals to diagnose the disease with a high rate of accuracy, there is an urgent need to create accurate and automated diagnosis systems capable of classifying human bio-signals. In this study, an automatic system, denoted as (CT-BS- Cov-Eig based FOA-F-SVM), has been proposed to detect the prevalence and health effects of alcoholism from multichannel electroencephalogram (EEG) signals. The EEG signals are segmented into small intervals, with each segment passed to a clustering technique-based bootstrap (CT-BS) for the selection of modeling samples. A covariance matrix method with its eigenvalues (Cov-Eig) is integrated with the CT-BS system and applied for useful feature extraction related to alcoholism. To select the most relevant features, a nonparametric approach is adopted, and to classify the extracted features, a radius-margin-based support vector machine (F-SVM) with a fruit fly optimization algorithm (FOA), (i.e., FOA-F-SVM) is utilized. To assess the performance of the proposed CT-BS model, different types of evaluation methods are employed, and the proposed model is compared with the state-of-the-art models to benchmark the overall effectiveness of the newly designed system for EEG signals. The results in this study show that the proposed CT-BS model is more effective than the other commonly used methods and yields a high accuracy rate of 99%. In comparison with the state-of-the-art algorithms tested on identical databases describing the capability of the newly proposed FOA-F-SVM method, the study ascertains the proposed model as a promising medical diagnostic tool with potential implementation in automated alcoholism detection systems used by clinicians and other health practitioners. The proposed model, adopted as an expert system where EEG data could be classified through advanced pattern recognition techniques, can assist neurologists and other health professionals in the accurate and reliable diagnosis and treatment decisions related to alcoholism.

Effective Connectivity for Default Mode Network Analysis of Alcoholism

Introduction: With the recent technical advances in brain imaging modalities such as magnetic resonance imaging, positron emission tomography, and functional magnetic resonance imaging (fMRI), researchers' interests have inclined over the years to study brain functions through the analysis of the variations in the statistical dependence among various brain regions. Through its wide use in studying brain connectivity, the low temporal resolution of the fMRI represented by the limited number of samples per second, in addition to its dependence on brain slow hemodynamic changes, makes it of limited capability in studying the fast underlying neural processes during information exchange between brain regions. Materials and Methods: In this article, the high temporal resolution of the electroencephalography (EEG) is utilized to estimate the effective connectivity within the default mode network (DMN). The EEG data are collected from 20 subjects with alcoholism and 25 healthy subjects (controls), and used to obtain the effective connectivity diagram of the DMN using the Partial Directed Coherence algorithm. Results: The resulting effective connectivity diagram within the DMN shows the unidirectional causal effect of each region on the other. The variations in the causal effects within the DMN between controls and alcoholics show clear correlation with the symptoms that are usually associated with alcoholism, such as cognitive and memory impairments, executive control, and attention deficiency. The correlation between the exchanged causal effects within the DMN and symptoms related to alcoholism is discussed and properly analyzed. Conclusion: The establishment of the causal differences between control and alcoholic subjects within the DMN regions provides valuable insight into the mechanism by which alcohol modulates our cognitive and executive functions and creates better possibility for effective treatment of alcohol use disorder.

Quantifying the dynamics of electroencephalographic (EEG) signals to distinguish alcoholic and non-alcoholic subjects using an MSE based K-d tree algorithm

In this paper, we have employed K-d tree algorithmic based multiscale entropy analysis (MSE) to distinguish alcoholic subjects from non-alcoholic ones. Traditional MSE techniques have been used in many applications to quantify the dynamics of physiological time series at multiple temporal scales. However, this algorithm requires O(N2), i.e. exponential time and space complexity which is inefficient for long-term correlations and online application purposes. In the current study, we have employed a recently developed K-d tree approach to compute the entropy at multiple temporal scales. The probability function in the entropy term was converted into an orthogonal range. This study aims to quantify the dynamics of the electroencephalogram (EEG) signals to distinguish the alcoholic subjects from control subjects, by inspecting various coarse grained sequences formed at different time scales, using traditional MSE and comparing the results with fast MSE (fMSE). The performance was also measured in terms of specificity, sensitivity, total accuracy and receiver operating characteristics (ROC). Our findings show that fMSE, with a K-d tree algorithmic approach, improves the reliability of the entropy estimation in comparison with the traditional MSE. Moreover, this new technique is more promising to characterize the physiological changes having an affect at multiple time scales.