Detection of Epilepsy Disease Using Support Vector Machine Based on EEG Signals

Epilepsy disease related to nervous system of the brain characterised by unusual brain activity, including seizures. The EEG signals are transcription of sensation activity in the brain that are widely used in a variety of healthcare implementations, including the detection of epileptic seizures. Detailed examination was traditionally used by neurologists to time to make inferences. This approach was generally take too much time intensive, and the results were dependent on the reader’s expertise. As a result, automatic epileptic seizure monitoring systems that can provide successful performance in a short amount of time are required. A machine learning classifier was used to differentiate between epileptic and non-epileptic seizure. This paper provides an overview of how to detect epilepsy automatically using EEG signals by using support vector machine learning classification method. In this study, we employ a support vector machine classifier to identify epilepsy using EEG inputs. Accuracy, Sensitivity, Recall, and F1 score were used to evaluate classifier performance. The simulation results shows an accuracy of 82% by using given approach.