Seizure Prediction Using Multi-View Features and Improved Convolutional Gated Recurrent Network

Epilepsy is one of the most common neurological diseases worldwide. Early prediction of seizure onsets is of great significance for the safety of intractable epilepsy patients. This work aims to develop a reliable and accurate method for patient-specific seizure prediction based on scalp electroencephalograms (EEGs). Local fractal spectrum, relative band energy, and synchronization modularity features are used to reveal the characteristics of multi-channel EEG in perspectives of time domain, frequency domain, and functional connectivity, respectively. A novel framework, named multi-view convolutional gated recurrent network (Mv-CGRN), is proposed to comprehensively analyze the spatio-temporal sequences of multi-view features and capture the potential variations preceding the impending seizure. Moreover, an attention mechanism is embedded in Mv-CGRN to determine the optimal feature combinations for each patient by adaptively tuning the weight parameters. The proposed system achieves an average sensitivity of 94.50% and an average false positive rate (FPR) of 0.118/h on CHB-MIT scalp EEG dataset, using the leave-one-out cross validation (LOOCV). Our work shows a promising performance compared with the state-of-the-art works in the same filed.

[1]  Luyao Wang,et al.  Beta-Band Functional Connectivity Influences Audiovisual Integration in Older Age: An EEG Study , 2017, Front. Aging Neurosci..

[2]  Ning Wang,et al.  Extracting and Selecting Distinctive EEG Features for Efficient Epileptic Seizure Prediction , 2015, IEEE Journal of Biomedical and Health Informatics.

[3]  Antonio Turiel,et al.  Microcanonical multifractal formalism—a geometrical approach to multifractal systems: Part I. Singularity analysis , 2008 .

[4]  Andreas Schulze-Bonhage,et al.  Epileptic seizure predictors based on computational intelligence techniques: A comparative study with 278 patients , 2014, Comput. Methods Programs Biomed..

[5]  Yadong Wang,et al.  Prediction for High Risk Clinical Symptoms of Epilepsy Based on Deep Learning Algorithm , 2018, IEEE Access.

[6]  Massimo Filippi,et al.  Structural Brain Connectome and Cognitive Impairment in Parkinson Disease. , 2017, Radiology.

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

[8]  K. Lehnertz,et al.  Seizure prediction and the preseizure period , 2002, Current opinion in neurology.

[9]  Bo Chen,et al.  MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications , 2017, ArXiv.

[10]  W. Art Chaovalitwongse,et al.  Adaptive epileptic seizure prediction system , 2003, IEEE Transactions on Biomedical Engineering.

[11]  Espen A F Ihlen,et al.  Multifractal analyses of response time series: A comparative study , 2013, Behavior research methods.

[12]  Manoranjan Paul,et al.  Seizure Prediction Using Undulated Global and Local Features , 2017, IEEE Transactions on Biomedical Engineering.

[13]  Dumitru Erhan,et al.  Going deeper with convolutions , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[14]  Jeffrey M. Hausdorff,et al.  Physionet: Components of a New Research Resource for Complex Physiologic Signals". Circu-lation Vol , 2000 .

[15]  Nitesh V. Chawla,et al.  Editorial: special issue on learning from imbalanced data sets , 2004, SKDD.

[16]  Alexandros Karatzoglou,et al.  Session-based Recommendations with Recurrent Neural Networks , 2015, ICLR.

[17]  Kwang-Hyun Cho,et al.  Predicting epileptic seizures from scalp EEG based on attractor state analysis , 2017, Comput. Methods Programs Biomed..

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

[19]  Reza Tafreshi,et al.  Predicting Epileptic Seizures in Scalp EEG Based on a Variational Bayesian Gaussian Mixture Model of Zero-Crossing Intervals , 2013, IEEE Transactions on Biomedical Engineering.

[20]  Aamir Saeed Malik,et al.  An EEG-based functional connectivity measure for automatic detection of alcohol use disorder , 2017, Artif. Intell. Medicine.

[21]  F. Mormann,et al.  Seizure prediction: the long and winding road. , 2007, Brain : a journal of neurology.

[22]  Luigi Chisci,et al.  Real-Time Epileptic Seizure Prediction Using AR Models and Support Vector Machines , 2010, IEEE Transactions on Biomedical Engineering.

[23]  F. L. D. Silva,et al.  EEG signal processing , 2000, Clinical Neurophysiology.

[24]  Keshab K. Parhi,et al.  Seizure prediction with bipolar spectral power features using Adaboost and SVM classifiers , 2013, 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[25]  Manoranjan Paul,et al.  Epileptic Seizure Prediction by Exploiting Spatiotemporal Relationship of EEG Signals Using Phase Correlation , 2016, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[26]  François Chollet,et al.  Xception: Deep Learning with Depthwise Separable Convolutions , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[27]  Yoshua Bengio,et al.  Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling , 2014, ArXiv.

[28]  Sarp Erturk,et al.  Seizure Prediction in Scalp EEG Using 3D Convolutional Neural Networks With an Image-Based Approach , 2019, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[29]  Benjamin H. Brinkmann,et al.  SVM-Based System for Prediction of Epileptic Seizures From iEEG Signal , 2017, IEEE Transactions on Biomedical Engineering.

[30]  Alexandros Karatzoglou,et al.  Personalizing Session-based Recommendations with Hierarchical Recurrent Neural Networks , 2017, RecSys.

[31]  Jianbo Gao,et al.  Multiplicative multifractal modeling and discrimination of human neuronal activity [rapid communication] , 2005 .

[32]  Chien-Liang Liu,et al.  Epileptic Seizure Prediction With Multi-View Convolutional Neural Networks , 2019, IEEE Access.

[33]  Mojtaba Bandarabadi,et al.  Epileptic seizure prediction using relative spectral power features , 2015, Clinical Neurophysiology.

[34]  Muhammad Tariq,et al.  Detection of Epilepsy Seizures in Neo-Natal EEG Using LSTM Architecture , 2019, IEEE Access.

[35]  Hang Su,et al.  Deep C-LSTM Neural Network for Epileptic Seizure and Tumor Detection Using High-Dimension EEG Signals , 2020, IEEE Access.

[36]  M E J Newman,et al.  Finding and evaluating community structure in networks. , 2003, Physical review. E, Statistical, nonlinear, and soft matter physics.

[37]  Joseph Picone,et al.  Gated recurrent networks for seizure detection , 2017, 2017 IEEE Signal Processing in Medicine and Biology Symposium (SPMB).

[38]  Lihan Tang,et al.  Accurate classification of epilepsy seizure types using wavelet packet decomposition and local detrended fluctuation analysis , 2020, Electronics Letters.

[39]  Nacim Betrouni,et al.  Fractal and multifractal analysis: A review , 2009, Medical Image Anal..

[40]  Gahangir Hossain,et al.  Seizure Prediction and Detection via Phase and Amplitude Lock Values , 2016, Front. Hum. Neurosci..

[41]  Keshab K. Parhi,et al.  Low-Complexity Seizure Prediction From iEEG/sEEG Using Spectral Power and Ratios of Spectral Power , 2016, IEEE Transactions on Biomedical Circuits and Systems.

[42]  Jongin Kim,et al.  EEG-Based Prediction of Epileptic Seizures Using Phase Synchronization Elicited from Noise-Assisted Multivariate Empirical Mode Decomposition , 2017, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

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

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

[45]  F. Varela,et al.  Measuring phase synchrony in brain signals , 1999, Human brain mapping.

[46]  Omid Kavehei,et al.  Epileptic Seizure Forecasting With Generative Adversarial Networks , 2019, IEEE Access.