Classification Algorithms Used In The Study of EEG-Based Epileptic Seizure Detection

Abstract

Epilepsy is a neurological illness that has become more frequent around the world. Nearly 80% of epileptic seizure sufferers live in low- and middle-income nations. In persons with encephalopathy, the risk of dying prematurely is three times higher than in the general population. Three-quarters of people with brain illnesses in low-income countries do not receive the treatment they require. Recurrent seizures are a symptom of epilepsy, characterized by strange bursts of excess energy in mind. Experts agree that most people diagnosed with epilepsy may be managed successfully, provided the episodes are discovered early on. As a result, machine learning plays an essential role in seizure detection and diagnosis. Support Vector Machine(SVM), Extreme Gradient Boosting(Xgboost), Decision Tree Classifier, Linear Discriminant Analysis(LDA), Perceptron, Naive Bayes Classifier, k-Nearest Neighbor(k-NN), and Logistic Regression are eight of the most widely used machine learning classification algorithms used to classify EEG based mostly Epileptic Seizures. Almost all classifiers, according to the study, give an efficient process. Despite this, the results show that SVM is the most effective method for detecting epileptic seizures, with a 96.84% accuracy rate. For diagnosing Epileptic Seizures using EEG signals, the perceptron model has a lower accuracy of 76.21% percent.