Name
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.Conclusions: The research provides a high comprehensive performance epileptic prediction method with a Fl 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. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1016/j.bspc.2020.102293 | BIOMEDICAL SIGNAL PROCESSING AND CONTROL |
Keywords | DocType | Volume |
Epilepsy, Prediction, Electroencephalograms, Sample entropy, Bi-LSTM | Journal | 64 |
ISSN | Citations | PageRank |
1746-8094 | 0 | 0.34 |
References | Authors | |
0 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Qizhong Zhang | 1 | 0 | 0.34 |
| Ji Ding | 2 | 0 | 0.34 |
| Wanzeng Kong | 3 | 91 | 22.56 |
| Liu Yang | 4 | 200 | 33.54 |
| Qian Wang | 5 | 108 | 22.12 |
| Tiejia Jiang | 6 | 3 | 1.49 |