A Patient-Specific Method for Epileptic Seizure Prediction During Sleep Based on Deep Neural Network
CHENG Chenchen1,3, YOU Bo1,2, LIU Yan2,3,4, DAI Yakang3,4
1. School of Mechanical and Power Engineering, Harbin University of Science and Technology, Harbin 150080 2. School of Automation, Harbin University of Science and Technology, Harbin 150080 3. Medical Imaging Technology Laboratory, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163 4. Suzhou Key Laboratory of Medical and Health Information Technology, Suzhou 215163
Abstract The existing epileptic seizure prediction methods present the problems of low accuracy, high false alarm rate, sleep electroencephalogram(EEG) specificity of epileptic patients and differences in EEG signals caused by differences in the location and type of epileptic foci . In this paper, a patient-specific method for epileptic seizure prediction during sleep based on deep neural network is proposed to help doctors and patients to take timely and effective treatment measures. Consequently, the probability of patients suffering from complications and sudden death is reduced. The original EEG signals are filtered and segmented to remove noise and trigger the alarm in a short time. Discrete wavelet transform is utilized to decompose the EEG, and statistical features are extracted to reveal the time-frequency characteristics of EEG signals. Then, the bi-direction long-short term memory(Bi-LSTM) is employed to mine the most discriminative features combined with the leave-one-out method for classification. The prediction results are obtained after the optimization of the decision-making process. Experiments with different frequency band restrictions show that the δ band signal related to sleep epilepsy affects the prediction performance and the performance of the proposed method is better than the existing sleep epileptic seizure prediction methods.
Corresponding Authors:
DAI Yakang, Ph.D., researcher. His research interests include intelligent medical image processing and analysis.
About author:: CHENG Chenchen, Ph.D. candidate. Her research interests include neural electrophysiological signal analysis, machine lear-ning and pattern recognition. YOU Bo, Ph.D., professor. His research interests include robot, neural electrophysiological signal analysis, machine learning and pattern recognition. LIU Yan, Ph.D., associate professor. Her research interests include multi-modal neuroimaging intelligent processing and analysis.
CHENG Chenchen,YOU Bo,LIU Yan等. A Patient-Specific Method for Epileptic Seizure Prediction During Sleep Based on Deep Neural Network[J]. , 2021, 34(4): 333-342.
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2021, Vol. 34 
