ResneXt-Lenet: A hybrid deep learning for epileptic seizure prediction

Encephalopathy is the result of epilepsy, which is defined as recurring seizures. Around the world, almost 65 million people suffer with epilepsy. Because an epileptic seizure involves a crucial clinical element and a clear contradiction with everyday activities, it can be difficult to predict it. The electroencephalogram (EEG) has been the established signal for clinical evaluation of brain activities. So far, several methodologies for the detection of epileptic seizures have been proposed but have not been effective. To bridge this gap, a powerful model for epileptic seizure prediction using ResneXt-LeNet is proposed. Here, a Kalman filter is used to preprocess the EEG signal to reduce noise levels in the signal. Then, feature extraction is performed to extract features, such as statistical and spectral. Feature selection is done using Fuzzy information gain that suggests appropriate choices for future processing, and finally, seizure prediction is done using hybrid ResneXt-LeNet, which is a combination of ResneXt and Lenet. The proposed ResneXt-LeNet achieved excellent performance with a maximum accuracy of 98.14%, a maximum sensitivity of 98.10%, and a specificity of 98.56%.