Electrical status epilepticus during sleep electroencephalogram waveform identification and analysis based on a graph convolutional neural network

Abstract

Electrical status epilepticus during sleep (ESES) is an epileptic syndrome in which neurons in the brain continue to discharge during the sleep phase and is common in mid-childhood. Affected patients often experience a decline in cognition, learning ability, memory, and expressive language skills. Therefore, timely and accurate diagnosis can effectively protect the health of a patient. Currently, the identification and analysis of ESES activities mainly rely on manual detection or traditional matching learning algorithms, such as morphology and template matching. These algorithms are time-consuming or have low accuracy. Therefore, in this paper, we propose a graph convolutional neural network that can automatically and accurately identify ESES activity from non-ESES activity. We divide the whole EEG signal into small segments, each of which covers one second of the EEG data. Then, we construct a graph according to each segment of the EEG data and train a graph convolutional neural network to classify the graph into two categories: ESES or non-ESES. Compared with other state-of-the-art algorithms, for the proposed algorithm, the accuracy, F1-Score, Area Under Curve(AUC) and sensitivity reaches 91.2%, 95.0%, 96.5%, and 91.3%, respectively, and outperforms the other algorithms.