A Novel Dynamic Update Framework for Epileptic Seizure Prediction

Epileptic seizure prediction is a difficult problem in clinical applications, and it has the potential to significantly improve the patients' daily lives whose seizures cannot be controlled by either drugs or surgery. However, most current studies of epileptic seizure prediction focus on high sensitivity and low false-positive rate only and lack the flexibility for a variety of epileptic seizures and patients' physical conditions. Therefore, a novel dynamic update framework for epileptic seizure prediction is proposed in this paper. In this framework, two basic sample pools are constructed and updated dynamically. Furthermore, the prediction model can be updated to be the most appropriate one for the prediction of seizures' arrival. Mahalanobis distance is introduced in this part to solve the problem of side information, measuring the distance between two data sets. In addition, a multichannel feature extraction method based on Hilbert-Huang transform and extreme learning machine is utilized to extract the features of a patient's preseizure state against the normal state. At last, a dynamic update epileptic seizure prediction system is built up. Simulations on Freiburg database show that the proposed system has a better performance than the one without update. The research of this paper is significantly helpful for clinical applications, especially for the exploitation of online portable devices.

[1]  C. Elger,et al.  Epileptic Seizures and Epilepsy: Definitions Proposed by the International League Against Epilepsy (ILAE) and the International Bureau for Epilepsy (IBE) , 2005, Epilepsia.

[2]  Leonidas D. Iasemidis,et al.  Measuring resetting of brain dynamics at epileptic seizures: application of global optimization and spatial synchronization techniques , 2009, J. Comb. Optim..

[3]  Rodrigo Quian Quiroga,et al.  Nonlinear multivariate analysis of neurophysiological signals , 2005, Progress in Neurobiology.

[4]  Bin He,et al.  Electrophysiological Imaging of Brain Activity and Connectivity—Challenges and Opportunities , 2011, IEEE Transactions on Biomedical Engineering.

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

[6]  F. H. Lopes da Silva,et al.  The Impact of EEG/MEG Signal Processing and Modeling in the Diagnostic and Management of Epilepsy , 2008, IEEE Reviews in Biomedical Engineering.

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

[8]  F. Mormann,et al.  Epileptic seizures are preceded by a decrease in synchronization , 2003, Epilepsy Research.

[9]  K. Lehnertz,et al.  Synchronization phenomena in human epileptic brain networks , 2009, Journal of Neuroscience Methods.

[10]  Chee Kheong Siew,et al.  Extreme learning machine: Theory and applications , 2006, Neurocomputing.

[11]  Heng Guo,et al.  Temporal lobe seizure prediction based on a complex Gaussian wavelet , 2011, Clinical Neurophysiology.

[12]  Rong Yan,et al.  On the value of pairwise constraints in classification and consistency , 2007, ICML '07.

[13]  James R. Williamson,et al.  Epileptic seizure prediction using the spatiotemporal correlation structure of intracranial EEG , 2011, 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[14]  P. Flandrin,et al.  Empirical Mode Decomposition , 2012 .

[15]  C. M. Lim,et al.  Analysis of epileptic EEG signals using higher order spectra , 2009, Journal of medical engineering & technology.

[16]  Wang Ming.  Epileptic Seizure Prediction Based on Probabilistic Discriminative Extreme Leaning Machine , 2012 .

[17]  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.

[18]  A. Aarabi,et al.  EEG seizure prediction: Measures and challenges , 2009, 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[19]  Xinying Wang,et al.  Semi-supervised weighted distance metric learning for kNN classification , 2010, 2010 International Conference on Computer, Mechatronics, Control and Electronic Engineering.

[20]  Thorsten Joachims,et al.  Learning a Distance Metric from Relative Comparisons , 2003, NIPS.

[21]  John S Duncan,et al.  Adult epilepsy , 2006, The Lancet.

[22]  Torleiv Kløve,et al.  Permutation Arrays Under the Chebyshev Distance , 2009, IEEE Transactions on Information Theory.

[23]  Atsushi Iwata,et al.  Stochastic Computing Chip for Measurement of Manhattan Distance , 2005 .

[24]  Narasimhan Sundararajan,et al.  A Fast and Accurate Online Sequential Learning Algorithm for Feedforward Networks , 2006, IEEE Transactions on Neural Networks.

[25]  Hong Bao,et al.  GPGPU-Aided Ensemble Empirical-Mode Decomposition for EEG Analysis During Anesthesia , 2010, IEEE Transactions on Information Technology in Biomedicine.

[26]  Xin-Ping Guan,et al.  Application of wavelet-based similarity analysis to epileptic seizures prediction , 2007, Comput. Biol. Medicine.

[27]  Xiaodong Liu,et al.  DBCAMM: A novel density based clustering algorithm via using the Mahalanobis metric , 2012, Appl. Soft Comput..

[28]  K Lehnertz,et al.  Using bivariate signal analysis to characterize the epileptic focus: the benefit of surrogates. , 2011, Physical review. E, Statistical, nonlinear, and soft matter physics.

[29]  A. Schulze-Bonhage,et al.  Joining the benefits: Combining epileptic seizure prediction methods , 2010, Epilepsia.

[30]  Feiping Nie,et al.  Learning a Mahalanobis distance metric for data clustering and classification , 2008, Pattern Recognit..

[31]  Wei Wang,et al.  Comparing Study of Nonlinear Model for Epileptic Preictal Prediction , 2010, 2010 4th International Conference on Bioinformatics and Biomedical Engineering.

[32]  A. Schulze-Bonhage,et al.  The seizure prediction characteristic: a general framework to assess and compare seizure prediction methods , 2003, Epilepsy & Behavior.

[33]  F. Mormann,et al.  Mean phase coherence as a measure for phase synchronization and its application to the EEG of epilepsy patients , 2000 .

[34]  Michael I. Jordan,et al.  Distance Metric Learning with Application to Clustering with Side-Information , 2002, NIPS.

[35]  Renato Cordeiro de Amorim,et al.  Minkowski metric, feature weighting and anomalous cluster initializing in K-Means clustering , 2012, Pattern Recognit..

[36]  Gaoxiang Ouyang,et al.  Nonlinear similarity analysis for epileptic seizures prediction , 2006 .

[37]  W. Hauser,et al.  Comment on Epileptic Seizures and Epilepsy: Definitions Proposed by the International League Against Epilepsy (ILAE) and the International Bureau for Epilepsy (IBE) , 2005, Epilepsia.

[38]  Dong Sun Park,et al.  Online sequential extreme learning machine with forgetting mechanism , 2012, Neurocomputing.

[39]  Nittaya Kerdprasop,et al.  Approximate Web Database Search Based on Euclidean Distance Measurement , 2011 .

[40]  Reza Fazel-Rezai,et al.  Fuzzy rule-based seizure prediction based on correlation dimension changes in intracranial EEG , 2010, 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology.

[41]  Shanbao Tong,et al.  Phase Synchronization Analysis of EEG Signals: An Evaluation Based on Surrogate Tests , 2012, IEEE Transactions on Biomedical Engineering.

[42]  Bin He,et al.  A rule-based seizure prediction method for focal neocortical epilepsy , 2012, Clinical Neurophysiology.

[43]  Ning Wang,et al.  Exploration of instantaneous amplitude and frequency features for epileptic seizure prediction , 2012, 2012 IEEE 12th International Conference on Bioinformatics & Bioengineering (BIBE).

[44]  A. Schulze-Bonhage,et al.  Comparison of three nonlinear seizure prediction methods by means of the seizure prediction characteristic , 2004 .

[45]  Gabriel Rilling,et al.  On empirical mode decomposition and its algorithms , 2003 .

[46]  Jacques Lemoine,et al.  A Spectral Approach for Sifting Process in Empirical Mode Decomposition , 2010, IEEE Transactions on Signal Processing.

[47]  A. Schulze-Bonhage,et al.  Spatio-temporal patient–individual assessment of synchronization changes for epileptic seizure prediction , 2006, Clinical Neurophysiology.

[48]  Gabriela Jager,et al.  Fast Empirical Mode Decompositions of Multivariate Data Based on Adaptive spline-Wavelets and a Generalization of the Hilbert-Huang-Transformation (HHT) to Arbitrary Space Dimensions , 2010, Adv. Data Sci. Adapt. Anal..

[49]  Xingyuan Wang,et al.  Researches on chaos phenomenon of EEG dynamics model , 2006, Appl. Math. Comput..