Advanced Detection of Pre-Ictal Stages in Epilepsy Using Convolutional Neural Networks and Automated Feature Extraction.

During a seizure, the neurons in the brain are firing abnormally. It is important to identify the symptoms to stop the spread of the virus, as a late diagnosis greatly increases the risk of infecting others. The domain is still heavily biased towards the use of just a few publicly available scalp EEG datasets (for instance, CHB/MIT) and has barely scratched the surface in relation to the use of iEEGs with little consideration for multi-centre or private datasets. Class imbalance, where preictals outnumber interictals and icts, also presents a challenge in the training process due to the non-stationarity, variability, and presence of noise in the EEG data. The study used the pre-processed data and the convolution neural network model to predict the duration of the pre-ictal phase of an epilepsy attack. To this end, the CHB-MIT gathering dataset is used. Hand-crafted features usually work with conventional methods. The suggested approach comprises signal metadata processing, an automatic feature extraction tool supported by signal characteristic identification and LSTM and SVM-based result classification. The proposed model defines the pre-ictal state considering sensitivity and specificity. Initially the detection system was configured for a two-second window duration.