Classification of Epileptic Encephalogram Signals Using Area of Octagon

Epilepsy is a nervous disease that’s generally detected via EEG signal acquired from the human brain. Nowadays Doctors are facing many challenges in the diagnosis of epilepsy. In order to reduce their burden we are introducing a new method to classify epilepsy. The proposed method makes use of EMD along with area of octagon technique for the classification of Epileptic and epileptic free EEG signals. Empirical Mode Decomposition (EMD) decomposes Epileptic and epileptic free EEG signals into many intrinsic mode functions (IMFs).The difference equations of IMFs are plotted which looks similar to octagon shape. The Area of Octagon (AOO) method is measured for the obtained octagon shape has been used as feature set in order to distinguish epileptic from epileptic free EEG signals. The feature set obtained by AOO method of first four IMFs namely IMFI, IMF2, IMF3, IMF4 are used for the classification of Epileptic and Epileptic free EEG signals using Support vector machine (SVM) such as Linear SVM, Quadratic SVM (Q-SVM) and Fine Gaussian SVM (FG-SVM) classifiers which provides minimum of 99% to maximum of 100% accuracy in the classification of Epilepsy which is better when compared to the existing methods. The proposed approach may be useful for the neurosurgeons to perceive epileptic regions of the patient mind.

[1]  W. Art Chaovalitwongse,et al.  Adaptive epileptic seizure prediction system , 2003, IEEE Transactions on Biomedical Engineering.

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

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

[4]  Ram Bilas Pachori,et al.  Analysis of normal and epileptic seizure EEG signals using empirical mode decomposition , 2011, Comput. Methods Programs Biomed..

[5]  Ahmad Taher Azar,et al.  Performance analysis of support vector machines classifiers in breast cancer mammography recognition , 2013, Neural Computing and Applications.

[6]  Yuanlin Zhang,et al.  A New Approach to Automated Epileptic Diagnosis Using EEG and Probabilistic Neural Network , 2008, 2008 20th IEEE International Conference on Tools with Artificial Intelligence.

[7]  Osman Erogul,et al.  Epileptic EEG detection using the linear prediction error energy , 2010, Expert Syst. Appl..

[8]  Ram Bilas Pachori,et al.  Epileptic seizure detection based on the instantaneous area of analytic intrinsic mode functions of EEG signals , 2013 .

[9]  Rajeev Sharma,et al.  Classification of epileptic seizures in EEG signals based on phase space representation of intrinsic mode functions , 2015, Expert Syst. Appl..

[10]  Rami J Oweis,et al.  Seizure classification in EEG signals utilizing Hilbert-Huang transform , 2011, Biomedical engineering online.

[11]  Xinghua Liu,et al.  Diagnosis of Breast Tumours and Evaluation of Prognostic Risk by Using Machine Learning Approaches , 2007, ICIC.

[12]  Shufang Li,et al.  Feature extraction and recognition of ictal EEG using EMD and SVM , 2013, Comput. Biol. Medicine.

[13]  Adriano O Andrade,et al.  Study of age-related changes in postural control during quiet standing through Linear Discriminant Analysis , 2009, Biomedical engineering online.

[14]  Ram Bilas Pachori,et al.  Discrimination between Ictal and Seizure-Free EEG Signals Using Empirical Mode Decomposition , 2008, J. Electr. Comput. Eng..

[15]  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.

[16]  Ram Bilas Pachori,et al.  Epileptic seizure classification in EEG signals using second-order difference plot of intrinsic mode functions , 2014, Comput. Methods Programs Biomed..

[17]  Ram Bilas Pachori,et al.  Classification of ictal and seizure-free EEG signals using fractional linear prediction , 2014, Biomed. Signal Process. Control..

[18]  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.