Epilepsy prediction through optimized multidimensional sample entropy and Bi-LSTM

Abstract

ObjectiveEpilepsy 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. MethodsIn 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. ResultsComparing 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. ConclusionsThe 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.