Epileptic Seizure Recognition Using Convolutional Neural Networks and Transfer Learning

With the deepening of research on the electrical activity of epileptic neurons, electromagnetic stimulation therapy for epileptic patients has attracted considerable attention. Automatic and accurate identification of epileptic seizure status is the key to the timely and accurate implementation of electromagnetic stimulation. In this study, a novel patient-specific seizure state recognition technique based on convolutional neural network(CNN)and transfer learning is proposed. First, on the basis of the electroencephalogram(EEG)recordings from multiple patients, the one-dimensional CNN is used to establish a general model for epileptic seizure state recognition. The general model is used to learn the common characteristics of EEG during seizures in different patients to achieve general recognition of seizure states. Then, on the basis of the EEG recordings from individual patients, the parameters of the general model are transferred to the personalized model using transfer learning to simplify model training and accelerate convergence. The model performance of the overall migration and convolution layer parameter migration modes of universal model parameters to the personalized model is also discussed. Finally, the algorithm is applied to long-term scalp EEG recordings of 17 patients in the CHB-MIT database. The average accuracy of all patient personalized models reaches 91.04%. On the basis of the personalized model, the patients' long-term EEG recordings are used to judge the onset and end of seizures. The detection rates of the onset and end of seizure states reach 96.43% and 89.29%, respectively, in the test dataset. Thus, the EEG-based seizure state recognition model using CNN and transfer learning could be used in the development of treatment programs for patients with epilepsy. © 2021, Editorial Board of Journal of Tianjin University(Science and Technology). All right reserved.