Real-Time Epileptic Seizure Prediction Method With Spatio-Temporal Information Transfer Learning

The accurate prediction of epileptic seizures is a significant challenge in the field of epilepsy. Despite numerous studies devoted to improving the prediction accuracy, there are still some difficulties in the application of current methods in clinical practice, such as high computational cost, poor real-time performance, and over-reliance on labeled data. To address these issues, a real-time seizure prediction method with spatio-temporal information transfer learning (RTSPM-STITL) has been proposed in this study. In the RTSPM-STITL method, the human brain is regarded as a time-varying high-dimensional neurodynamic system, in which epileptic seizures are viewed as state transitions caused by time-varying system parameters. Specifically, the spatio-temporal information transfer (STIT) model is firstly constructed by the recurrent neural network (RNN) and trained by the Force Learning (a realtime learning mechanism). Then the trained STIT model is utilized to transform the high-dimensional neurodynamic system data into low-dimensional time series to capture the dynamic features of epileptic seizures. Also, the critical slowing down effect (CSD) of the dynamic features of epileptic seizures is utilized to detect seizure warning signals. The experimental results demonstrate that the proposed method can achieve higher accuracy and sensitivity without labeled data on both the CHB-MIT and Siena scalp EEG databases. Especially, the parameters of the STIT model can be updated in real-time based on patient data, without iterative training. More importantly, the STIT model can maintain high sensitivity and accuracy with only 48400 parameters, which is reduced by more than 91% compared with contrast models in this experiment. Therefore, the proposed method can significantly reduce the computational cost and accurately predict epileptic seizures, as well as with high real-time, practicality, applicability, and interpretability.