Detection of epileptic seizure using hybrid machine learning algorithms
P Velvizhy, Ria Bas Len, N. Rajeshwari, K. Kanimozhi
Abstract
A brain condition known as epilepsy is characterized by frequent seizures. A seizure is an abrupt change in behavior brought on by a brief disturbance in the electrical activity of the brain. For those who are experiencing seizures, abrupt and synchronized electrical energy bursts that impact their consciousness, movements, and sensations disturb the usual electrical pattern. Even now, it is simple to detect an epileptic seizure automatically because the existing detection techniques are time-consuming and inaccurate. In order to effectively identify seizures, machine learning and deep learning algorithms are integrated. Using features retrieved from EEG data where the MLP+CNN+SVM model was used, we present a method in this study for detecting epileptic seizures from EEG signals using three hybrid machine learning classification networks, namely SVM+CNN, MLP+CNN, and SVM+MLP+CNN.