Application of 1-D CNN to Predict Epileptic Seizures using EEG Records

Abstract

Epilepsy is a disorder in the electrical activity of the brain that occurs in a specific area or even the entire brain. These changes are visible through the acquisition of electroencephalogram (EEG) brain signals. EEG signals are important tools in predicting epilepsy because they are noninvasive measurement and display electrical activity at different external nodes at human brain. We used the CHB-MIT EEG Database in this study to develop an artificial model to predict epileptic seizures. Thus, we applied a one-dimensional convolutional neural network (CNN) to investigate raw EEG signals as an important indicator for starting time of a seizure. The seven-layer CNN was used to detect Preictal and Interictal states of brain where the performance of the proposed model was evaluated in terms of accuracy, specificity, and sensitivity which resulted in 97%, 98.47%, and 98.5%, respectively. Moreover, the proposed model was trained in two different feeding states: 1-Feeding by individual channel, 2-Feeding by grouped channels. It seems that the obtained results are promising.