Robust Seizure Prediction Based on Multivariate Empirical Mode Decomposition and Maximum Synchronization Modularity

Reliable and timely seizure prediction has been increasingly helpful and indispensable for epileptic patients, ensuring safety and improving life quality. Based on electroencephalogram (EEG), a new patient-specific seizure prediction method is proposed in this paper to detect impending seizures automatically and accurately, using a novel indicator called maximum synchronization modularity. As the first step towards this goal, raw EEG signals are decomposed by multivariate empirical mode decomposition (MEMD). Then graph community detection algorithm is applied to characterize the phase synchronization modularity of sub-band EEG signals. Thus, the deep interaction of scalp electrical activity can be effectively revealed. Finally, radial basis function neural network (RBFNN) is used for the classification. The proposed method achieves an average prediction accuracy of 99.06% and an average sensitivity of 100% on CHB-MIT scalp EEG database, outperforming related works based on the same database.

[1]  Norden E. Huang,et al.  Ensemble Empirical Mode Decomposition: a Noise-Assisted Data Analysis Method , 2009, Adv. Data Sci. Adapt. Anal..

[2]  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.

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

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

[5]  N. Huang,et al.  The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis , 1998, Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences.

[6]  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.

[7]  A. Kraskov,et al.  On the predictability of epileptic seizures , 2005, Clinical Neurophysiology.

[8]  P. Pattnaik,et al.  Pediatric Seizure prediction from EEG signals based on unsupervised learning techniques using various distance measures , 2017, 2017 1st International Conference on Electronics, Materials Engineering and Nano-Technology (IEMENTech).

[9]  W. Singer,et al.  Neural Synchrony in Brain Disorders: Relevance for Cognitive Dysfunctions and Pathophysiology , 2006, Neuron.

[10]  Weidong Zhou,et al.  Epileptic Seizure Detection Using Lacunarity and Bayesian Linear Discriminant Analysis in Intracranial EEG , 2013, IEEE Transactions on Biomedical Engineering.

[11]  N. Swerdlow,et al.  Gamma Band Oscillations Reveal Neural Network Cortical Coherence Dysfunction in Schizophrenia Patients , 2006, Biological Psychiatry.

[12]  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.

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

[14]  Florian Mormann,et al.  Seizure prediction , 2008, Scholarpedia.

[15]  Danilo P. Mandic,et al.  Emd via mEMD: multivariate noise-Aided Computation of Standard EMD , 2013, Adv. Data Sci. Adapt. Anal..

[16]  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.

[17]  Parvathy Prathap,et al.  EEG spectral feature based seizure prediction using an efficient sparse classifier , 2017, 2017 International Conference on Intelligent Computing, Instrumentation and Control Technologies (ICICICT).

[18]  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.

[19]  D. Broomhead,et al.  Radial Basis Functions, Multi-Variable Functional Interpolation and Adaptive Networks , 1988 .

[20]  Yi Liu,et al.  Hilbert-Huang Transform and the Application , 2020, 2020 IEEE International Conference on Artificial Intelligence and Information Systems (ICAIIS).

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

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

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

[24]  Lizhe Wang,et al.  An EEMD-ICA Approach to Enhancing Artifact Rejection for Noisy Multivariate Neural Data , 2016, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[25]  D. P. Mandic,et al.  Multivariate empirical mode decomposition , 2010, Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences.

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

[27]  Liang-Gee Chen,et al.  Seizure prediction based on classification of EEG synchronization patterns with on-line retraining and post-processing scheme , 2011, 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.