Seizure detection from multi-channel EEG using entropy-based dynamic graph embedding

An epileptic seizure is a chronic disease with sudden abnormal discharge of brain neurons, which leads to transient brain dysfunction. To detect epileptic seizures, we propose a novel idea based on a dynamic graph embedding model. The dynamic graph is built by identifying the correlation among the multi-channel EEG signals. Graph entropy measurement is exploited to calculate the similarity among the graph at each time interval and construct the graph embedding space. Since the abnormal electrical brain activity causes the epileptic seizure, the graph entropy during the seizure time interval is different from other time intervals. Therefore, we propose an entropy-based dynamic graph embedding model to cluster the graphs, and the graphs with epileptic seizures are discriminated. We applied the proposed approach to the Children Hospital Boston-Massachusetts Institute of Technology Scalp EEG database. The results have shown that the proposed approach outperformed the baselines by 1.4% with respect to accuracy.

Automated summary

In this study, a novel approach based on dynamic graph embedding model was proposed to detect epileptic seizures by identifying correlation among multi-channel EEG signals and constructing graph embedding space. By calculating graph entropy and clustering the graphs, the method effectively discriminated graphs with epileptic seizures. The proposed approach outperformed baselines by 1.4% in accuracy when applied to Scalp EEG database.