Automated & enhanced epileptic seizure detection based on deep learning based architecture

Abstract

A precise seizure detection system allows epileptic patients to receive early warnings before a seizure occurs. It is critical for people who are drug-resistant. To find the very minimal time before seizure onset, traditional seizure prediction techniques rely on variables collected from electroencephalography (EEG) recordings and classification algorithms. Such methods cannot achieve high-accuracy prediction due to the information loss of hand-crafted features and the limited classification capabilities of regression and other algorithms. Kernels are employed in the early and late stages of the CNN RNN architecture with VGG 16 in the convolution and max-pooling layers, respectively. The suggested hybrid model is tested using the CHB-MIT scalp EEG datasets. The total sensitivity, false prediction rate, and area under the receiver operating characteristic have all yielded positive results.