Epileptic Focus Localization Using Discrete Wavelet Transform Based on Interictal Intracranial EEG

Over the past decade, with the development of machine learning, discrete wavelet transform (DWT) has been widely used in computer-aided epileptic electroencephalography (EEG) signal analysis as a powerful time-frequency tool. But some important problems have not yet been benefitted from DWT, including epileptic focus localization, a key task in epilepsy diagnosis and treatment. Additionally, the parameters and settings for DWT are chosen empirically or arbitrarily in previous work. In this work, we propose a framework to use DWT and support vector machine (SVM) for epileptic focus localization problem based on EEG. To provide a guideline in selecting the best settings for DWT, we decompose the EEG segments in seven commonly used wavelet families to their maximum theoretical levels. The wavelet and its level of decomposition providing the highest accuracy in each wavelet family are then used in a grid search for obtaining the optimal frequency bands and wavelet coefficient features. Our approach achieves promising performance on two widely-recognized intrancranial EEG datasets that are also seizure-free, with an accuracy of 83.07% on the Bern-Barcelona dataset and an accuracy of 88.00% on the UBonn dataset. Compared with existing DWT-based approaches in epileptic EEG analysis, the proposed approach leads to more accurate and robust results. A guideline for DWT parameter setting is provided at the end of the paper.

[1]  Daniel Rivero,et al.  Epileptic seizure detection using multiwavelet transform based approximate entropy and artificial neural networks , 2010, Journal of Neuroscience Methods.

[2]  Duo Chen,et al.  Epileptic focus localization using EEG based on discrete wavelet transform through full-level decomposition , 2015, 2015 IEEE 25th International Workshop on Machine Learning for Signal Processing (MLSP).

[3]  Jean Gotman,et al.  Combining EEG and fMRI in the study of epileptic discharges , 2011, Epilepsia.

[4]  J. Gotman,et al.  Wavelet based automatic seizure detection in intracerebral electroencephalogram , 2003, Clinical Neurophysiology.

[5]  Stéphane Mallat,et al.  Characterization of Signals from Multiscale Edges , 2011, IEEE Trans. Pattern Anal. Mach. Intell..

[6]  Sigmund Jenssen,et al.  Treatment and Management of Epilepsy in the Elderly Demented Patient , 2010, American journal of Alzheimer's disease and other dementias.

[7]  Jedediah M. Singer,et al.  Quickest detection of drug-resistant seizures: An optimal control approach , 2011, Epilepsy & Behavior.

[8]  Mostefa Mesbah,et al.  A matching pursuit-based signal complexity measure for the analysis of newborn EEG , 2007, Medical & Biological Engineering & Computing.

[9]  F. L. D. Silva,et al.  Event-related EEG/MEG synchronization and desynchronization: basic principles , 1999, Clinical Neurophysiology.

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

[11]  Weidong Zhou,et al.  Automatic Seizure Detection Using Wavelet Transform and SVM in Long-Term Intracranial EEG , 2012, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[12]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[13]  N. Crone,et al.  Network dynamics of the brain and influence of the epileptic seizure onset zone , 2014, Proceedings of the National Academy of Sciences.

[14]  Olivier Chapelle,et al.  Training a Support Vector Machine in the Primal , 2007, Neural Computation.

[15]  K. Tsakalis,et al.  Information Flow and Application to Epileptogenic Focus Localization From Intracranial EEG , 2009, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[16]  Divyakant Agrawal,et al.  A comparison of DFT and DWT based similarity search in time-series databases , 2000, CIKM '00.

[17]  Mauro Ursino,et al.  A wavelet-based energetic approach for the analysis of biomedical signals: Application to the electroencephalogram and electro-oculogram , 2009, Appl. Math. Comput..

[18]  Clodoaldo Ap. M. Lima,et al.  Automatic EEG signal classification for epilepsy diagnosis with Relevance Vector Machines , 2009, Expert Syst. Appl..

[19]  Stéphane Mallat,et al.  A Theory for Multiresolution Signal Decomposition: The Wavelet Representation , 1989, IEEE Trans. Pattern Anal. Mach. Intell..

[20]  Elif Derya Übeyli.  Analysis of EEG signals by combining eigenvector methods and multiclass support vector machines , 2008, Comput. Biol. Medicine.

[21]  João Paulo Papa,et al.  EEG signal classification for epilepsy diagnosis via optimum path forest - A systematic assessment , 2014, Neurocomputing.

[22]  K Lehnertz,et al.  Indications of nonlinear deterministic and finite-dimensional structures in time series of brain electrical activity: dependence on recording region and brain state. , 2001, Physical review. E, Statistical, nonlinear, and soft matter physics.

[23]  U. Rajendra Acharya,et al.  Application of Entropy Measures on Intrinsic Mode Functions for the Automated Identification of Focal Electroencephalogram Signals , 2015, Entropy.

[24]  M G Marciani,et al.  Lateralization of the epileptogenic focus by computerized EEG study and neuropsychological evaluation. , 1992, The International journal of neuroscience.

[25]  H. Adeli,et al.  Wavelet-based EEG processing for computer-aided seizure detection and epilepsy diagnosis , 2015, Seizure.

[26]  U. Rajendra Acharya,et al.  Use of principal component analysis for automatic classification of epileptic EEG activities in wavelet framework , 2012, Expert Syst. Appl..

[27]  J. Gotman.  High frequency oscillations: The new EEG frontier , 2013, Epilepsy & Behavior.

[28]  E. Carrette,et al.  Functional brain connectivity from EEG in epilepsy: Seizure prediction and epileptogenic focus localization , 2014, Progress in Neurobiology.

[29]  Ralph G Andrzejak,et al.  Nonrandomness, nonlinear dependence, and nonstationarity of electroencephalographic recordings from epilepsy patients. , 2012, Physical review. E, Statistical, nonlinear, and soft matter physics.

[30]  Sridevi V. Sarma,et al.  Physiology of functional and effective networks in epilepsy , 2015, Clinical Neurophysiology.

[31]  Ali H. Shoeb,et al.  An algorithm for seizure onset detection using intracranial EEG , 2011, Epilepsy & Behavior.

[32]  Mark R. Bower,et al.  Synchrony in normal and focal epileptic brain: the seizure onset zone is functionally disconnected. , 2010, Journal of neurophysiology.

[33]  Clodoaldo Ap. M. Lima,et al.  Kernel machines for epilepsy diagnosis via EEG signal classification: A comparative study , 2011, Artif. Intell. Medicine.

[34]  A. Aarabi,et al.  A fuzzy rule-based system for epileptic seizure detection in intracranial EEG , 2009, Clinical Neurophysiology.

[35]  Elif Derya Übeyli.  Combined neural network model employing wavelet coefficients for EEG signals classification , 2009, Digit. Signal Process..

[36]  Fabrice Wendling,et al.  Localization of Epileptogenic Zone on Pre-surgical Intracranial EEG Recordings: Toward a Validation of Quantitative Signal Analysis Approaches , 2014, Brain Topography.

[37]  Keshab K. Parhi,et al.  Early Seizure Detection Using Neuronal Potential Similarity: A Generalized Low-Complexity and Robust Measure , 2015, Int. J. Neural Syst..

[38]  Elif Derya íbeyli.  Analysis of EEG signals by combining eigenvector methods and multiclass support vector machines , 2008 .

[39]  S. Santaniello,et al.  Optimal Control-Based Bayesian Detection of Clinical and Behavioral State Transitions , 2012, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[40]  J. H. Cross,et al.  ILAE Official Report: A practical clinical definition of epilepsy , 2014, Epilepsia.

[41]  H. Adeli,et al.  Analysis of EEG records in an epileptic patient using wavelet transform , 2003, Journal of Neuroscience Methods.

[42]  U. Rajendra Acharya,et al.  An Integrated Index for the Identification of Focal Electroencephalogram Signals Using Discrete Wavelet Transform and Entropy Measures , 2015, Entropy.

[43]  J. Gotman,et al.  Discriminating preictal and interictal states in patients with temporal lobe epilepsy using wavelet analysis of intracerebral EEG , 2012, Clinical Neurophysiology.