Spectral Entropy for Epileptic Seizures Detection

The electroencephalogram (EEG) is the brain signal that represented the valuable information about the brains condition. The configuration of the signals waveform may contain valuable and useful information about the different states of the brain. Since the biological signals are personal, indications may occur highly random in both time and frequency domains. Thus the computer analyzing is necessary. EEG is decomposed by wavelet transform and coefficient sets are obtained. In this paper spectral entropy is applied to these coefficient sets for epileptic seizures detection. This process is applied to three different groups of EEG signals: 1) healthy states, 2) epileptic states during a seizure-free interval (interictal EEG), 3) epileptic states during a seizure (ictal EEG). At the end the statistical analysis is applied for distinguishing the coefficient sets. This statistical process can differentiate between ictal and healthy subject (with eyes close) of cD2 coefficients (15-30 Hz) with 99% p-value.

[1]  C. Elger,et al.  Spatio-temporal dynamics of the primary epileptogenic area in temporal lobe epilepsy characterized by neuronal complexity loss. , 1995, Electroencephalography and clinical neurophysiology.

[2]  Ingrid Daubechies,et al.  Ten Lectures on Wavelets , 1992 .

[3]  Prognosis.  Guidelines for Epidemiologic Studies on Epilepsy , 1993, Epilepsia.

[4]  Bruce J. West,et al.  Chaos and fractals in human physiology. , 1990, Scientific American.

[5]  Hojjat Adeli,et al.  Fuzzy clustering approach for accurate embedding dimension identification in chaotic time series , 2003, Integr. Comput. Aided Eng..

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

[7]  L D Iasemidis,et al.  Non-linearity in invasive EEG recordings from patients with temporal lobe epilepsy. , 1997, Electroencephalography and clinical neurophysiology.

[8]  Leonidas D. Iasemidis,et al.  Transition to epileptic seizures: Optimization , 1999, Discrete Mathematical Problems with Medical Applications.

[9]  Hojjat Adeli,et al.  Wavelet-Chaos Methodology for Analysis of EEGs and EEG Sub-Bands , 2010 .

[10]  C. King.  Fractal and chaotic dynamics in nervous systems , 1991, Progress in Neurobiology.

[11]  Leonidas D. Iasemidis,et al.  The evolution with time of the spatial distribution of the largest Lyapunov exponent on the human epileptic cortex , 1991 .

[12]  Hongkui Jing,et al.  Topographic analysis of dimension estimates of EEG and filtered rhythms in epileptic patients with complex partial seizures , 2000, Biological Cybernetics.

[13]  Sang Joon Kim,et al.  A Mathematical Theory of Communication , 2006 .

[14]  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.

[15]  N. Sriraam,et al.  Automated detection of epileptic seizures using wavelet entropy feature with recurrent neural network classifier , 2008, TENCON 2008 - 2008 IEEE Region 10 Conference.

[16]  Bai-lian Li,et al.  Wavelet Analysis of Coherent Structures at the Atmosphere-Forest Interface. , 1993 .

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

[18]  Stéphane Mallat,et al.  A Theory for Multiresolution Signal Decomposition: The Wavelet Representation , 1989, IEEE Trans. Pattern Anal. Mach. Intell..

[19]  M. Victor Wickerhauser,et al.  Wavelets: Algorithms and Applications (Yves Meyer) , 1994, SIAM Rev..

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