Patient-Specific Seizure Prediction Using the Convolutional Neural Networks

An epileptic seizure is a disease of the central nervous system caused by abnormal activity generated by neurons in the brain. Seizure reduces the quality of life of epileptic patients due to unconsciousness. In this paper, an efficient seizure prediction system is proposed to improve the quality of life. The raw EEG signal is converted into the EEG signal image. Then, a convolutional neural network is used for training the prediction system. The performance of the proposed system is evaluated using the CHB-MIT dataset. The classification accuracy of interictal and preictal states is achieved up to 94.33% using 10-fold cross-validation. Due to the presence of noise in the EEG signal, a pool based technique is used to make the decision on the majority of a 1 min EEG signal that increase the accuracy of the prediction of upcoming seizures.

[1]  Mahmoud I. Khalil,et al.  Epileptic seizure prediction using zero-crossings analysis of EEG wavelet detail coefficients , 2016, 2016 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB).

[2]  Haidar Khan,et al.  Focal Onset Seizure Prediction Using Convolutional Networks , 2018, IEEE Transactions on Biomedical Engineering.

[3]  Saleh A. Alshebeili,et al.  Epileptic Seizure Prediction Using CSP and LDA for Scalp EEG Signals , 2017, Comput. Intell. Neurosci..

[4]  Jiawei Yang,et al.  Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram , 2018, Neural Networks.

[5]  Fei Wang,et al.  Poster paper: Predicting seizures from electroencephalography recordings: A knowledge transfer strategy , 2016, 2016 IEEE International Conference on Healthcare Informatics (ICHI).

[6]  James R. Williamson,et al.  Seizure prediction using EEG spatiotemporal correlation structure , 2012, Epilepsy & Behavior.

[7]  Miguel Angelo de Abreu de Sousa,et al.  Epileptic Seizure Prediction from EEG Signals Using Unsupervised Learning and a Polling-Based Decision Process , 2018, ICANN.

[8]  Zeeshan Syed,et al.  Multi-task seizure detection: addressing intra-patient variation in seizure morphologies , 2016, Machine Learning.

[9]  Yoshua Bengio,et al.  Convolutional networks for images, speech, and time series , 1998 .

[10]  Junjie Chen,et al.  The detection of epileptic seizure signals based on fuzzy entropy , 2015, Journal of Neuroscience Methods.

[11]  Zhong Liu,et al.  A Novel Ensemble Method for Imbalanced Data Learning: Bagging of Extrapolation-SMOTE SVM , 2017, Comput. Intell. Neurosci..

[12]  Theoden Netoff,et al.  Seizure prediction with spectral power of EEG using cost‐sensitive support vector machines , 2011, Epilepsia.

[13]  P. Genton,et al.  Current limitations of antiepileptic drug therapy: a conference review , 2003, Epilepsy Research.

[14]  Manoranjan Paul,et al.  Epileptic seizure detection by analyzing EEG signals using different transformation techniques , 2014, Neurocomputing.