Epileptic EEG signal analysis and identification based on nonlinear features

In this paper, two non-linear complexity measures, namely approximate entropy and sample entropy are investigated as feature extraction methods for evaluating the regularity of the epileptic EEG signals. Furthermore, in order to obtain more efficient feature extraction for EEG signals, an optimized algorithm for sample entropy measure (O-SampEn) is proposed which removes the calculation redundancy and optimizes the computation procedure for sample entropy measure. Clinical EEG data was obtained from 20 intracranial electrodes placed within the epileptogenic zone in five epilepsy patients during both interictal and ictal periods. In terms of the experimental results, both sample entropy and approximate entropy analysis show lower values during epileptic seizures, which mean an increase of EEG signal regularity during ictal state. Compared with approximate entropy, the feature extraction based on sample entropy measure is more sensitive to EEG signal variety caused by epileptic seizures, approximately 10.14%~20.02% higher than the results using approximate entropy. In addition, the proposed optimized algorithm for sample entropy can run 9.52~36.16 times faster than the original sample entropy algorithm according to the simulation. High discrimination ability and fast computation speed of the proposed optimized sample entropy algorithm demonstrate its huge potential as a novel feature extraction method for real-time epileptic seizure detection.

[1]  G. Ouyang,et al.  Predictability analysis of absence seizures with permutation entropy , 2007, Epilepsy Research.

[2]  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.

[3]  N. Birbaumer,et al.  Permutation entropy to detect vigilance changes and preictal states from scalp EEG in epileptic patients. A preliminary study , 2008, Neurological Sciences.

[4]  J. Richman,et al.  Physiological time-series analysis using approximate entropy and sample entropy. , 2000, American journal of physiology. Heart and circulatory physiology.

[5]  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.

[6]  Duoqian Miao,et al.  Best basis-based wavelet packet entropy feature extraction and hierarchical EEG classification for epileptic detection , 2011, Expert Syst. Appl..

[7]  Xinnian Chen,et al.  Comparison of the Use of Approximate Entropy and Sample Entropy: Applications to Neural Respiratory Signal , 2005, 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference.

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

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

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

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

[12]  Elif Derya Übeyli.  Automatic detection of electroencephalographic changes using adaptive neuro-fuzzy inference system employing Lyapunov exponents , 2009, Expert systems with applications.

[13]  N Pradhan,et al.  Detection of seizure activity in EEG by an artificial neural network: a preliminary study. , 1996, Computers and biomedical research, an international journal.

[14]  F. L. D. Silva,et al.  Dynamics of the human alpha rhythm: evidence for non-linearity? , 1999, Clinical Neurophysiology.

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

[16]  L M Hively,et al.  Detecting dynamical changes in time series using the permutation entropy. , 2004, Physical review. E, Statistical, nonlinear, and soft matter physics.

[17]  Scott B. Wilson,et al.  Seizure detection: correlation of human experts , 2003, Clinical Neurophysiology.

[18]  Elif Derya íbeyli.  Automatic detection of electroencephalographic changes using adaptive neuro-fuzzy inference system employing Lyapunov exponents , 2009 .

[19]  Annette M. Molinaro,et al.  Prediction error estimation: a comparison of resampling methods , 2005, Bioinform..