An Efficient Hybrid Model for Patient-Independent Seizure Prediction Using Deep Learning

被引:9
作者
Halawa, Rowan Ihab [1 ]
Youssef, Sherin M. [1 ]
Elagamy, Mazen Nabil [1 ]
机构
[1] Arab Acad Sci & Technol AAST, Coll Engn & Technol, Comp Engn Dept, Alexandria 1029, Egypt
来源
APPLIED SCIENCES-BASEL | 2022年 / 12卷 / 11期
关键词
epilepsy; detection; inter-ictal; ictal; pre-ictal; EEG signal; deep learning; 1-D CNN;
D O I
10.3390/app12115516
中图分类号
O6 [化学];
学科分类号
0703 ;
摘要
Recently, many researchers have deployed different deep learning techniques to predict epileptic seizure, using electroencephalogram signals. However, most of this research requires very large amounts of memory and complicated feature extraction algorithms. In addition, they could not precisely examine EEG signal characteristics, which led to poor prediction performance. In this research, a non-patient-specific epileptic seizure prediction approach is proposed. The proposed model integrates Wavelet-based EEG signal processing with deep learning architectures for efficient prediction of pre-ictal and inter-ictal signals. The proposed system uses different models of one-dimensional convolutional neural networks to discriminate between inter-ictal signal and pre-ictal signals in order to enhance prediction performance. Experiments have been carried out on a benchmark dataset to validate the robustness of the proposed model. The experimental results showed that the proposed approach achieved 93.4% for 16 patients and 97.87% for 6 patients. Experiments showed that the proposed model can predict epileptic seizures effectively, which can have remarkable potential in clinical applications.
引用
收藏
页数:15
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