EEG Subband Analysis using Approximate Entropy for the Detection of Epilepsy

Epilepsy is a neurological disorder which affects the nervous system. Epileptic seizures are due to sudden hyperactivity in certain parts of the brain. Electroencephalogram (EEG) is the commonly used modality for the detection of epilepsy. Automatic seizure detection helps in diagnosis and monitoring of epilepsy especially during long term recordings of EEG. This paper presents non linear analysis of EEG for the detection of epilepsy using approximate entropy (ApEn). The proposed method involves ApEn measured from EEG subbands applied as features to an artificial neural network (ANN) classifier. The ApEn measured from delta, theta, alpha, beta and gamma subbands of normal EEG, ictal and inter ictal EEGs are used as features. In the present work detection of epilepsy is considered as a two class problem. In the first case the classification is done between normal and ictal EEGs and in the second case, classification is done between normal and inter ictal EEGs. For both cases artificial neural networks with back propagation training are used as classifiers. The classification accuracy of 100% is obtained for normal and ictal groups and that of 98.9% is obtained for normal and inters ictal EEGs.

[1]  S M Pincus,et al.  Approximate entropy as a measure of system complexity. , 1991, Proceedings of the National Academy of Sciences of the United States of America.

[2]  Daniel Graupe,et al.  A neural-network-based detection of epilepsy , 2004, Neurological research.

[3]  Hojjat Adeli,et al.  Principal Component Analysis-Enhanced Cosine Radial Basis Function Neural Network for Robust Epilepsy and Seizure Detection , 2008, IEEE Transactions on Biomedical Engineering.

[4]  Eric Laciar,et al.  Multiparametric detection of epileptic seizures using Empirical Mode Decomposition of EEG records , 2010, 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology.

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

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

[7]  R. Hornero,et al.  Non-linear Analysis of Intracranial Electroencephalogram Recordings with Approximate Entropy and Lempel-Ziv Complexity for Epileptic Seizure Detection , 2007, 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[8]  V. Udayashankara,et al.  Neural network classifier for the detection of epilepsy , 2013, 2013 International conference on Circuits, Controls and Communications (CCUBE).

[9]  Manolis Tsiknakis,et al.  An approach to absence epileptic seizures detection using Approximate Entropy , 2013, 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[10]  Saeid Sanei,et al.  EEG signal processing , 2000, Clinical Neurophysiology.

[11]  Hojjat Adeli,et al.  A Wavelet-Chaos Methodology for Analysis of EEGs and EEG Subbands to Detect Seizure and Epilepsy , 2007, IEEE Transactions on Biomedical Engineering.

[12]  V. Srinivasan,et al.  Approximate Entropy-Based Epileptic EEG Detection Using Artificial Neural Networks , 2007, IEEE Transactions on Information Technology in Biomedicine.

[13]  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..

[14]  Ulrich Heute,et al.  Application of State-Space Modeling to instantaneous independent-component analysis , 2011, 2011 4th International Conference on Biomedical Engineering and Informatics (BMEI).

[15]  Yi Zhou,et al.  Localization of epileptic foci based on scalp EEG and approximate entropy , 2013, 2013 6th International Conference on Biomedical Engineering and Informatics.

[16]  N Radhakrishnan,et al.  Estimating regularity in epileptic seizure time-series data. A complexity-measure approach. , 1998, IEEE engineering in medicine and biology magazine : the quarterly magazine of the Engineering in Medicine & Biology Society.

[17]  Steven M. Pincus,et al.  Approximate entropy: a complexity measure for biological time series data , 1991, Proceedings of the 1991 IEEE Seventeenth Annual Northeast Bioengineering Conference.

[18]  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.

[19]  Dimitrios I. Fotiadis,et al.  Epileptic Seizure Detection in EEGs Using Time–Frequency Analysis , 2009, IEEE Transactions on Information Technology in Biomedicine.

[20]  M. Ismail Gursoy,et al.  Regularization and kernel parameters optimization based on PSO algorithm in EEG signals classification with SVM , 2011, 2011 IEEE 19th Signal Processing and Communications Applications Conference (SIU).

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

[22]  Min Han,et al.  EEG signal classification for epilepsy diagnosis based on AR model and RVM , 2010, 2010 International Conference on Intelligent Control and Information Processing.

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

[24]  Jian Zhang,et al.  Classifying Detection of Epileptic EEG Based on Approximate Entropy in Wavelet Domain , 2009, 2009 2nd International Conference on Biomedical Engineering and Informatics.

[25]  Abdulhamit Subasi,et al.  EEG signal classification using wavelet feature extraction and a mixture of expert model , 2007, Expert Syst. Appl..

[26]  Hamed Vavadi,et al.  A wavelet-approximate entropy method for epileptic activity detection from EEG and its sub-bands , 2010 .

[27]  V. Srinivasan,et al.  Artificial Neural Network Based Epileptic Detection Using Time-Domain and Frequency-Domain Features , 2005, Journal of Medical Systems.

[28]  Gregory A. Worrell,et al.  Modeling cortical source dynamics and interactions during seizure , 2011, 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.