Statistical Analysis of EEG Signals in Wavelet Domain for Efficient Seizure Prediction

Epilepsy is an electrophysiological disorder of the brain, characterized by recurrent seizures. Electroencephalogram (EEG) is a test that measures and records the electrical activity of the brain, and is widely used in the prediction and analysis of epileptic seizures. This paper provides a statistical analysis associated with EEG signals assuming that these signals can be categorized into inter-ictal, pre-ictal, and ictal states. We study histograms and cumulative histograms for segments of various signal states, in wavelet domain by utilizing different signal processing tools such as the differentiator and median filtering, as well as the local mean, and local variance estimators. The results show that signal states could be distinguished according to statistics in the wavelet domain.

[1]  Richard D. Jones,et al.  Detection of focal epileptiform activity in the EEG: an SVD and dipole model approach , 2002, Proceedings of the Second Joint 24th Annual Conference and the Annual Fall Meeting of the Biomedical Engineering Society] [Engineering in Medicine and Biology.

[2]  E. Abt.  Understanding statistics 3 , 2010, Evidence-Based Dentistry.

[3]  Hasan Al-Nashash,et al.  Prediction of PTZ-induced seizures using wavelet-based residual entropy of cortical and subcortical field potentials , 2003, IEEE Transactions on Biomedical Engineering.

[4]  Moncef Gabbouj,et al.  Weighted median filters: a tutorial , 1996 .

[5]  Bin He,et al.  A rule-based seizure prediction method for focal neocortical epilepsy , 2012, Clinical Neurophysiology.

[6]  Barry D. Van Veen,et al.  Seizure Detection Using the Phase-Slope Index and Multichannel ECoG , 2012, IEEE Transactions on Biomedical Engineering.

[7]  W. Art Chaovalitwongse,et al.  A novel reinforcement learning framework for online adaptive seizure prediction , 2010, 2010 IEEE International Conference on Bioinformatics and Biomedicine (BIBM).

[8]  H. Khamis,et al.  Frequency–moment signatures: A method for automated seizure detection from scalp EEG , 2013, Clinical Neurophysiology.

[9]  D. Freedman,et al.  On the histogram as a density estimator:L2 theory , 1981 .

[10]  A. Prochazka,et al.  Signal Analysis and Prediction , 1998 .

[11]  Fathi E. Abd El-Samie,et al.  Information Security for Automatic Speaker Identification , 2011 .

[12]  G. Pfurtscheller,et al.  Brain-Computer Interfaces for Communication and Control. , 2011, Communications of the ACM.

[13]  Weidong Zhou,et al.  Automatic Seizure Detection Using Wavelet Transform and SVM in Long-Term Intracranial EEG , 2012, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[14]  Banu Onaral,et al.  Detection of seizure onset using wavelet analysis , 1994, Proceedings of 16th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[15]  U. Rajendra Acharya,et al.  Automated EEG analysis of epilepsy: A review , 2013, Knowl. Based Syst..

[16]  A. S. Malik,et al.  Epileptic seizure detection using the singular values of EEG signals , 2013, 2013 ICME International Conference on Complex Medical Engineering.

[17]  David W. Scott,et al.  Multivariate Density Estimation: Theory, Practice, and Visualization , 1992, Wiley Series in Probability and Statistics.

[18]  Herbert A. Sturges,et al.  The Choice of a Class Interval , 1926 .

[19]  Sen M. Kuo,et al.  Real-time digital signal processing , 2001 .