Informatics in Medicine Unlocked (Jan 2021)
Classification of EEG signals for epileptic seizures detection and eye states identification using Jacobi polynomial transforms-based measures of complexity and least-square support vector machine
Abstract
Background and objectives: Epilepsy is the most prevalent neurological disorder in humans which is characterized by recurrent seizures resulting in neurologic, cognitive, psychological and social consequences. In addition, the automatic identification of brain conditions can be helpful in eye-brain-computer interface. That is why during these last few decades, many researchers focused on the development of classification systems for the automatic analysis and detection of epileptic seizures and eye states, which then can be integrated into implantable devices intended to detect the onset of seizures and trigger a focal treatment to block or suppress the seizures progression and to improve the living conditions of patients. In this impetus, this work aims to develop a novel automated classification system which can be performed to detect and identify whether electroencephalogram (EEG) signals belong to epileptic patients in seizure or seizure-free conditions, or to normal individuals with opened or closed eyes. Methods: The proposed classification system consisted of models based on Jacobi polynomial transforms (JPTs). Discrete Legendre transform (DLT) and discrete Chebychev transform (DChT) firstly extract the beta (β) and gamma (γ) rhythms of EEG signals. Thereafter, different measures of complexity are computed from the EEG signals and their extracted rhythms, and applied as inputs of the least-square support vector machine (LS-SVM) classifier with radial basis function (RBF) kernel. Results: The Kruskal-Wallis statistical test is performed and demonstrated that computed JPTs-based measures of complexity sufficiently discriminate EEG signals since their values are significantly higher for seizure EEG signals as compared to those of seizure-free and normal EEG signals with opened or closed eyes. In addition, these measures are used to construct eleven relevant classification problems using the LS-SVM which gained out area under curve (AUC) between 0.983 and 1 which is still proportional to maximum classification accuracies of 88.75% and 100% for normal with opened eyes versus normal with closed eyes, and normal versus seizure or seizure-free versus seizure classification problems, respectively. Conclusion: Overall, it is found that the proposed classification system extends to be less complex for practical applications and can be suitable for automatic epileptic seizures detection and eye states identification.
