An EEG abnormality detection algorithm based on graphic attention network

The incidence of brain diseases has increased yearly, threatening human life and health seriously. The Electroencephalogram (EEG) has been playing an important role in clinical practice for diagnosing brain diseases. However, due to the interference of noise and the limitations of manual observation, experts need to spend a lot of time and energy on EEG interpretation, and also is affected by subjective factors, which is prone to misjudgment. Therefore, the establishment of EEG-assisted diagnosis system is of great significance for the clinical diagnosis of brain diseases. With the application of artificial intelligence in EEG auxiliary system, researchers have proposed a series of EEG automatic analysis and anomaly detection algorithms based on deep learning. However, the existing algorithms still have some shortcomings such as inadequate extraction of potential spatio-temporal features in EEG signals. In this paper, the method based on GATs-LSTM is proposed. The comparative analysis of the final experiment shows that, It has demonstrated superior performance on the benchmark dataset with sensitive and specificity as 99.21%, 99.73% and 94.15%, 95.67%. The proposed work shows great results on the benchmark datasets and a big potential for clinics as a support system with medical experts monitoring the patients.