No abstract

Epilepsy is a chronic disorder of the central nervous system that occurs irregularly and unpredictably, due to the temporary electrical disturbances in the brain. According to World Health Organization (WHO), approximately 50 million people worldwide have epilepsy, making it one of the most common neurological diseases globally [1]. It predisposes individuals to experience recurrent seizures. Electroencephalogram (EEG) is a technique used to measure the electrical activity of the brain signals for the diagnosis of neurological disorders, and it also paves the way for seizure detection using scalp and intra-cranial EEGs as the input data. In this paper, we have proposed a method for non-linear feature based epileptic seizure detection by extracting five features namely Entropy, Mean, Skewness, Standard Deviation and Band Power. The classification techniques used are K-nearest neighbor (KNN) and Support vector machine (SVM) which gave an accuracy of 95.33% and 100% respectively.

No abstract

When it comes to the diagnosis and treatment of epilepsy, as well as the general quality of life of the patient, the electroencephalogram (EEG) is an oftenutilisedas auxiliary test to aid in the process. It is the primary diagnostic test for epilepsy because it gives a continuous assessment of brain function with great temporal resolution over a long period of time, making it an excellent tool for early detection of epilepsy. Specifically, in this paper, we propose the creation of two Simulink models that can generate synthetic EEG data while maintaining the statistical characteristics of the EEG. In addition, we present the evaluation of two characteristics such as mutual information and correlation coefficient, in order totest the characteristics of any such synthetic generated data. The characteristics proposed here are tested with standard data available on online repository. Apart from using these characteristics for testing the validity of synthetic data, we may use these characteristics as features for machine learning applications.