Detection of Pre-stage of Epileptic Seizure by Exploiting Temporal Correlation of EMD Decomposed EEG Signals

Epilepsy is one of the common neurological disorders characterized by a sudden and recurrent malfunction of the brain that is termed "seizure", affecting over 50 million individuals worldwide. The Electroencephalogram (EEG) is the most influential technique in detection of epileptic seizures. In recent years, many research works have been devoted to the detection of epileptic seizures based on analysis of EEG signals. Despite remarkable work on seizure detection, there is no generic seizure detection scheme which performs reasonably well for different patients and different brain locations. In this paper we present a generic approach for feature extraction of preictal (pre-stage of seizure onset) and interictal (period between seizures) EEG signals using empirical mode decomposition (EMD) along with discrete cosine transformation (DCT) by exploit temporal correlation for detection of preictal phase of epileptic seizure. Then least square support vector machine is applied on the features for classifications. Results demonstrate that our proposed method outperforms the state-of-the-art methods in terms of sensitivity, specificity and accuracy to classify preictal and interictal EEG signals to the benchmark dataset extracted from different brain locations of different patients. 

[1]  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.

[2]  Vladimir Cherkassky,et al.  The Nature Of Statistical Learning Theory , 1997, IEEE Trans. Neural Networks.

[3]  Johan A. K. Suykens,et al.  LS-SVMlab Toolbox User's Guide , 2010 .

[4]  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.

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

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

[7]  Hojjat Adeli,et al.  Mixed-Band Wavelet-Chaos-Neural Network Methodology for Epilepsy and Epileptic Seizure Detection , 2007, IEEE Transactions on Biomedical Engineering.

[8]  Kemal Polat,et al.  Classification of epileptiform EEG using a hybrid system based on decision tree classifier and fast Fourier transform , 2007, Appl. Math. Comput..

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

[10]  Johan A. K. Suykens,et al.  LS-SVMlab Toolbox User's Guide version 1.7 , 2003 .

[11]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[12]  R. Panda,et al.  Classification of EEG signal using wavelet transform and support vector machine for epileptic seizure diction , 2010, 2010 International Conference on Systems in Medicine and Biology.

[13]  Manoranjan Paul,et al.  CLASSIFICATION OF ICTAL AND INTERICTAL EEG SIGNALS , 2013, BioMed 2013.

[14]  Ram Bilas Pachori,et al.  Classification of Seizure and Nonseizure EEG Signals Using Empirical Mode Decomposition , 2012, IEEE Transactions on Information Technology in Biomedicine.

[15]  Qin Zhang,et al.  ν-Nonparallel support vector machine for pattern classification , 2014, Neural Computing and Applications.

[16]  PachoriRam Bilas,et al.  Classification of Seizure and Nonseizure EEG Signals Using Empirical Mode Decomposition , 2012 .

[17]  Wim Van Paesschen,et al.  Canonical Correlation Analysis Applied to Remove Muscle Artifacts From the Electroencephalogram , 2006, IEEE Transactions on Biomedical Engineering.

[18]  Junshui Ma,et al.  Muscle artifacts in multichannel EEG: Characteristics and reduction , 2012, Clinical Neurophysiology.

[19]  C. Teixeira,et al.  Preprocessing effects of 22 linear univariate features on the performance of seizure prediction methods , 2013, Journal of Neuroscience Methods.

[20]  Esen Yildirim,et al.  Patient specific seizure prediction algorithm using Hilbert-Huang Transform , 2012, Proceedings of 2012 IEEE-EMBS International Conference on Biomedical and Health Informatics.

[21]  Hasan Ocak,et al.  Optimal classification of epileptic seizures in EEG using wavelet analysis and genetic algorithm , 2008, Signal Process..

[22]  Patrick Kwan,et al.  Refractory epilepsy: mechanisms and solutions , 2006, Expert review of neurotherapeutics.

[23]  Johan A. K. Suykens,et al.  Least Squares Support Vector Machine Classifiers , 1999, Neural Processing Letters.

[24]  Reshma Khemchandani,et al.  Twin Support Vector Machines for Pattern Classification , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[25]  Haifeng Wang,et al.  Comparison of SVM and LS-SVM for Regression , 2005, 2005 International Conference on Neural Networks and Brain.

[26]  T. Sejnowski,et al.  Removing electroencephalographic artifacts by blind source separation. , 2000, Psychophysiology.

[27]  B. Litt,et al.  High-frequency oscillations and seizure generation in neocortical epilepsy. , 2004, Brain : a journal of neurology.

[28]  Matthias M. Müller,et al.  Human Gamma Band Activity and Perception of a Gestalt , 1999, The Journal of Neuroscience.