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

This is the authors' abstract. We don't add key points for this paper.

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.