Automated Identification of Epileptic and Alcoholic Eeg Signals Using Recurrence Quantification Analysis

Abstract

Epilepsy is a common neurological disorder characterized by recurrence seizures. Alcoholism causes organic changes in the brain, resulting in seizure attacks similar to epileptic fits. Hence, it is challenging to differentiate the cause of fits as epileptic or alcoholism, which is important for deciding on the treatment in the neurology ward. The focus of this paper is to automatically differentiate epileptic, normal, and alcoholic electroencephalogram (EEG) signals. As the EEG signals are non-linear and dynamic in nature, it is difficult to tell the subtle changes in these signals with the help of linear techniques or by the naked eye. Therefore, to analyze the normal (control), epileptic, and alcoholic EEG signals, two non-linear methods, such as recurrence plots (RPs) and then recurrence quantification analysis (RQA) are adopted. Approximately 10 RQA parameters have been used to classify the EEG signals into three distinct classes, i.e., normal, epileptic, and alcoholic. Six classifiers, such as support vector machine (SVM), radial basis probabilistic neural network (RBPNN), decision tree (DT), Gaussian mixture model (GMM), k-nearest neighbor (kNN), and fuzzy Sugeno classifiers have been developed to accomplish this task. Results show that the GMM classifier outperformed the other classifiers with a classification sensitivity of 99.6%, specificity of 98.3%, and accuracy of 98.6%.

Characterizing Electrocardiographic Changes During Pre-seizure Periods through Temporal and Spectral Features

“…The increase in TT means that the time that the system abides at a specific state during seizures is longer than interictal phase. This change, previously observed applying RQA analysis to EEG signals in epilepsy [16], could reflect the synchronization of neurons during seizures.…”

A review on EEG-based methods for screening and diagnosing alcohol use disorder

“…Organic changes in the brain were observed during alcoholism which leads to seizure like epileptic cases. Ng et al (2012) has implemented an automatic method to differentiate epileptic, controls and alcoholics using EEG with accuracy of 98.6%. However, the validity of data is doubtful because the study has utilized datasets from different sources with different experiment designs (oddball visual stimulus vs. eye closed resting), equipment (64 channels vs. 128 channels) and references systems (Cz vs. common average reference) without any indication about the synchronization between two datasets.…”

RECSAE: An interactive model of relevance cognitive load, spatial memory, ADHD and EEG for special educators and mental health professionals

“…Table 1 shows seven types of EEG waves, their frequencies, amplitudes and active brain regions under normal conditions [1]. The EEG waves are extensively studied to understand neurodevelopmental processes [2], neurological disorders [3], addictions [4], neuropsychiatry [5] and so on. The key utility of EEG is to map the dynamic cerebral functions.…”