Electroencephalogram Signal Classification for Automated Epileptic Seizure Detection Using Genetic Algorithm

Background: Epilepsy causes when the repeated seizure occurs in the brain. Electroencephalogram (EEG) test provides valuable information about the brain functions and can be useful to detect brain disorder, especially for epilepsy. In this study, application for an automated seizure detection model has been introduced successfully. Materials and Methods: The EEG signals are decomposed into sub-bands by discrete wavelet transform using db2 (daubechies) wavelet. The eight statistical features, the four gray level co-occurrence matrix and Renyi entropy estimation with four different degrees of order, are extracted from the raw EEG and its sub-bands. Genetic algorithm (GA) is used to select eight relevant features from the 16 dimension features. The model has been trained and tested using support vector machine (SVM) classifier successfully for EEG signals. The performance of the SVM classifier is evaluated for two different databases. Results: The study has been experimented through two different analyses and achieved satisfactory performance for automated seizure detection using relevant features as the input to the SVM classifier. Conclusion: Relevant features using GA give better accuracy performance for seizure detection.

[1]  Clodoaldo Ap. M. Lima,et al.  Tackling EEG signal classification with least squares support vector machines: A sensitivity analysis study , 2010, Comput. Biol. Medicine.

[2]  Abdulhamit Subasi,et al.  EEG signal classification using PCA, ICA, LDA and support vector machines , 2010, Expert Syst. Appl..

[3]  Elif Derya Übeyli,et al.  Multiclass Support Vector Machines for EEG-Signals Classification , 2007, IEEE Transactions on Information Technology in Biomedicine.

[4]  B. Santhi,et al.  Seizure Detection using SVM Classifier on EEG Signal , 2014 .

[5]  Bijaya K. Panigrahi,et al.  Discrete harmony search based expert model for epileptic seizure detection in electroencephalography , 2012, Expert Syst. Appl..

[6]  Tamer Demiralp,et al.  Classification of electroencephalogram signals with combined time and frequency features , 2011, Expert Syst. Appl..

[7]  U. Rajendra Acharya,et al.  Entropies for detection of epilepsy in EEG , 2005, Comput. Methods Programs Biomed..

[8]  Boualem Boashash,et al.  Estimating the number of components of a multicomponent nonstationary signal using the short-term time-frequency Rényi entropy , 2011, EURASIP J. Adv. Signal Process..

[9]  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.

[10]  Mahmut Ozer,et al.  EEG signals classification using the K-means clustering and a multilayer perceptron neural network model , 2011, Expert Syst. Appl..

[11]  Julius Georgiou,et al.  Detection of epileptic electroencephalogram based on Permutation Entropy and Support Vector Machines , 2012, Expert Syst. Appl..

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

[13]  Wlodzislaw Duch,et al.  Comparison of Shannon, Renyi and Tsallis Entropy Used in Decision Trees , 2006, ICAISC.

[14]  Nicoletta Saulig,et al.  Algorithm based on the short-term Rényi entropy and IF estimation for noisy EEG signals analysis , 2017, Comput. Biol. Medicine.

[15]  B. Suguna Nanthini,et al.  EEG Signal Analysis for Epileptic Seizure Detection Using Soft Computing Techniques , 2014 .

[16]  Abdulhamit Subasi,et al.  Automatic recognition of alertness level from EEG by using neural network and wavelet coefficients , 2005, Expert Syst. Appl..

[17]  B. Gilmartin,et al.  The application of analysis of variance (ANOVA) to different experimental designs in optometry , 2002, Ophthalmic & physiological optics : the journal of the British College of Ophthalmic Opticians.

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

[19]  S GhoshDastidar.  Models of EEG data mining and classification in temporal lobe epilepsy : wavelet-chaos-neural network methodology and spiking neural networks. , 2007 .

[20]  Mingtian Zhou,et al.  A two stages sparse SVM training , 2014, Int. J. Mach. Learn. Cybern..

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

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

[23]  Yan Li,et al.  Clustering technique-based least square support vector machine for EEG signal classification , 2011, Comput. Methods Programs Biomed..

[24]  F. Albregtsen.  Statistical Texture Measures Computed from Gray Level Coocurrence Matrices , 2008 .

[25]  B. Santhi,et al.  Different approaches of analysing EEG signals for seizure detection , 2015 .

[26]  Sheng-Fu Liang,et al.  Combination of EEG Complexity and Spectral Analysis for Epilepsy Diagnosis and Seizure Detection , 2010, EURASIP J. Adv. Signal Process..

[27]  Amitava Chatterjee,et al.  Cross-correlation aided support vector machine classifier for classification of EEG signals , 2009, Expert Syst. Appl..

[28]  Abdulhamit Subasi.  Automatic detection of epileptic seizure using dynamic fuzzy neural networks , 2006, Expert Syst. Appl..

[29]  Bijaya K. Panigrahi,et al.  A comparative study of wavelet families for EEG signal classification , 2011, Neurocomputing.

[30]  Hasan Ocak,et al.  Automatic detection of epileptic seizures in EEG using discrete wavelet transform and approximate entropy , 2009, Expert Syst. Appl..