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

• Published in: Frontiers in neuroinformatics Vol. 15; p. 808339
• Main Authors: Al-Hadeethi, Hanan, Abdulla, Shahab, Diykh, Mohammed, Deo, Ravinesh C, Green, Jonathan H
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Research Foundation 03-02-2022; Frontiers Media S.A
• Subjects: Accuracy; Alcohol use; Alcoholic beverages; Alcoholics; Alcoholism; Algorithms; Blood pressure; Brain research; Classification; covariance matrix; Datasets; Deep learning; Diagnosis; Drug abuse; EEG; Eigenvalues; eigenvalues and fruit fly optimization; electroencephalogram; Electroencephalography; Entropy; Feature selection; Hypertension; Machine learning; Medical personnel; Methods; Nervous system; Neural networks; Neuroscience; Optimization algorithms; Pattern recognition; support vector machine (SVM); Support vector machines; Time series; Wavelet transforms; alcoholism; support vector machine (SVM); electroencephalogram; eigenvalues and fruit fly optimization; covariance matrix
• ISSN: 1662-5196; 1662-5196
• DOI: 10.3389/fninf.2021.808339

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.