Epilepsy prediction through optimized multidimensional sample entropy and Bi-LSTM

Abstract

Objective: Epilepsy is a repetitive and transient brain dysfunction caused by abnormal discharge of brain neurons. Sudden epileptic seizures may affect the daily life of patients. Therefore, real-time monitoring and prediction of epilepsy has important clinical meaning. Methods: In this paper, the characteristics of M-SampEn were extracted from 23 EEG signals and M-SampEn was specifically optimized to enhance efficiency. Then the Bi-LSTM may predict the trend of M-SampEn. The predicted M-SampEn was classified to determine if an epileptic seizure is imminent. Results: Comparing the classification accuracy, sensitivity, specificity and PPV of SampEn and M-SampEn, M-SampEn is found to have better performance. The prediction time is 5 minutes. The results demonstrate an accuracy of 80.09% and a FPR of 0.26/h for epileptic seizure prediction. Comparison with existing method(s): The optimized multidimensional sample entropy presented in this paper is more able to distinguish between the normal state and ictal of epilepsy. This paper also proposes a backward prediction method that is different from traditional epileptic seizure prediction. Conclusions: The research provides a high comprehensive performance epileptic prediction method with a F1 score of 0.83. The accuracy of 80.09% and the FPR of 0.26/h prove that the proposed method is able to predict seizures.