MRP-Net: Seizure detection method based on modified recurrence plot and additive attention convolution neural network

Abstract

Electroencephalographic (EEG) signals play an important role in the detection of seizures in epilepsy, and accurate detection of seizures can buy patients valuable treatment time. However, most seizure detection methods ignore the nonlinear, implicit characteristics of EEG, which has some impact on the accuracy of detection. Therefore, in this paper, we propose an EEG epilepsy detection network (MRP-Net) based on modified recurrence plot (MRP) and additive attention convolution neural network. This network can fully take into account the nonlinear, occult characteristics of EEG which can be mapped to the two-dimensional plane and served as the input of additive attention convolution neural network to automatically learn, analyze, and extract the EEG characteristics of seizures. The performance of the proposed method was evaluated on the Bonn University dataset and the SWEC-ETHZ short-term dataset. Sensitivity, specificity and accuracy were 100% in multiple detection tasks in the University of Bonn single-channel EEG dataset. The sensitivity, specificity and accuracy of SWEC’s short-term, multi-channel EEG dataset were 99.77%, 99.57% and 99.69%, respectively, higher than the latest methods (3.76%, 4.73% and 4.27%). The results of the experiments show that the network in this paper is superior and universal in epilepsy detection.