Electroencephalogram data analysis using Convolutional Neural Networks and Gramian Angular Field

Abstract

Abstract. The paper proposes a binary classification model designed to analyze electroencephalograms data to detecting pathologies associated with epilepsy. The model is based on the Convolutional Neural Network. As input data for the neural network, images obtained by transforming the values of the original electroencephalograms time series based on the Gramian Angular Field matrix were used. The model was trained on data from the Temple University Hospital electroencephalograms Seizure Corpus open data source. The proposed model demonstrated high performance metrics: accuracy – 91%, precision – 92%, recall – 95%, F1-0.93.