JBiSE> Vol.3 No.3, March 2010
Views: 1,630    Downloads: 1,628

Extracting a seizure intensity index from one-channel EEG signal using bispectral and detrended fluctuation analysis

DownloadDownload as PDF (Size:1805KB) Full-Text HTML PP. 253-261   DOI: 10.4236/jbise.2010.33034

ABSTRACT

Epilepsy is a medical condition that produces seizures affecting a variety of mental and physical functions. Seizures can last from a few seconds to a few minutes. They can have many symptoms, from convulsions and loss of consciousness to blank staring, lip smacking, or jerking movements of arms and legs. If early warning signals of an upcoming seizure (diagnosis of preictal period) are detected, proper treatment can be applied to the patient to help prevent the seizure. In this research, an epileptic disorder has been divided into three subsets: Normal, Preictal (just before the seizure), and Ictal (during seizure). By using Detrended Fluctuation Analysis (DFA), Bispectral Analysis (BIS), and Standard Deviation (SD) three features from single-channel EEG signals have been derived in the foresaid groups. A fuzzy classifier is used to separate the three groups which can successfully separate them with a separation degree of 100% and further a fuzzy inference engine is used to extract a Seizure Intensity Index (SII) from the Electroencephalogram (EEG) signals of the three different states. One can apparently see the distinction of SII amounts between the three states. It is more important when one remembers that these results are just from single-channel EEG signal.

KEYWORDS


Cite this paper

Hosseini, P. , Shalbaf, R. and Nasrabadi, A. (2010) Extracting a seizure intensity index from one-channel EEG signal using bispectral and detrended fluctuation analysis. Journal of Biomedical Science and Engineering, 3, 253-261. doi: 10.4236/jbise.2010.33034.

References

[1] Hagihira, S., Takashina, M., Mori, T., Mashimo, T. and Yoshiya, I. (2001) Practical issues in bispectral analysis of electroencephalographic signals. Journal of Anesthesia Analgesia, 93, 966-970.
[2] Huang, L., Sun, Q., Cheng, J. and Huang, Y. (2003) Prediction of epileptic seizures using bispectrum analysis of electroencephalograms and artificial neural network. Proceedings of 25th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 3, 2947-2949.
[3] Tallach, R., Ball, D. and Jefferson, P. (2004) Monitoring seizures with the bispectral index. Journal of Anesthesia, 59, 1033-1034.
[4] Ye, S., Park, J., Kim, J., Jung, J., Jeon, A., Kim, I., Son, J., Nam, K., Baik, S., Ro, J. and Jeon G. (2009) Development for the Evaluation Index of an Anesthesia Depth using the Bispectrum Analysis. International Journal of Biological and Medical Sciences, 4, 67-70.
[5] Peng, C., Buldyrev, S., Havlin, S., Simons, M., Stanley, H. and Goldberger, A. (1994) Mosaic organization of DNA nucleotides. Journal of Physical Review E, 49, 1685-1689.
[6] Gifani, P., Rabiee, H., Hashemi, M., Taslimi, P. and Ghanbari, M. (2007) Optimal fractal-scaling analysis of human EEG dynamic for depth of anesthesia quantification. Journal of the Franklin Institute, 344, 212-229.
[7] Jospin, M., Caminal, P., Jensen, E., Litvan, H., Vallverdu, M., Struys, M., Vereecke, H. and Kaplan, D. (2007) Detrended fluctuation analysis of EEG as a measure of depth of anesthesia. IEEE Transactions on Biomedical Engineering, 54, 840-846.
[8] Andrzejak, R., Lehnertz, K., Rieke, C., Mormann, F., David, P. and Elger, C. (2001) Indications of nonlinear deterministic and finite dimensional structures in time series of brain electrical activity: Dependence on recording region and brain state. Journal of Physical Review E, 64, 061907.
[9] Kantelhardta, J., Bundea, E., Regoa, H., Havlinb, S. and Bundea, A. (2001) Detecting long-range correlations with detrended Fluctuation analysis. Journal of Physica A, 295, 441-444.
[10] Ishibuchi, H., Nozaki, K., Yamamoto, N. and Tanaka, H. (1995) Selecting fuzzy if-then rules for classification problems using genetic algorithms. IEEE Transactions on Fuzzy Systems, 3, 260-270.
[11] Ishibuchi, H. and Nakashima, T. (2001) Effect of rule weights in fuzzy rule-based classification systems. IEEE Transactions on Fuzzy Systems, 9, 506-515.
[12] Nakashima, T., Yokota, Y. and Ishibuchi, H. (2005) Learning fuzzy if-then rules for pattern classification with weighted training patterns. Proceedings of 4th Conference of the European Society for Fuzzy Logic and Technology and 11th Rencontres Francophones sur la Logique Floue et ses Applications, 1064-1069.
[13] Esmaeili, V., Assareh, A., Shamsollahi, M., Moradi, M. and Arefian, N. (2008) Estimating the depth of anesthesia using fuzzy soft computation applied to EEG features. Journal of Intelligent Data Analysis, 12, 393-407.
[14] Kannathal, N., Choo, M., Acharya, U. and Sadasivan, P. (2005) Entropies for detection of epilepsy in EEG. Journal of Computer Methods and Programs in Biomedicine, 80, 187-194.
[15] Guler, N., Ubeyli, E. and Guler, I. (2005) Recurrent neural networks employing Lyapunov exponents for EEG signals classification. Journal of Expert Systems with Applications, 29, 506-514.
[16] Subasi, A. (2007) EEG signal classification using wavelet feature extraction and a mixture of expert model. Journal of Expert Systems with Applications, 32, 1084- 1093.
[17] Dastidar, S., Adeli, H. and Dadmehr, N. (2007) Mixed- band wavelet-chaos-neural network methodology for epilepsy and epileptic seizure detection. IEEE Transactions on Biomedical Engineering, 54, 1545-1551.
[18] Polat, K. and Gunes, S. (2007) Classification of epileptiform EEG using a hybrid system based on decision tree classifier and fast Fourier transform. Journal of Applied Mathematics and Computation, 187, 1017-1026.
[19] Polat, K. and Gunes, S. (2008) Artificial immune recognition system with fuzzy resource allocation mechanism classifier, principal component analysis and FFT method based new hybrid automated identification system for classification of EEG signals. Journal of Expert Systems with Applications, 34, 2039-2048.

comments powered by Disqus

Copyright © 2014 by authors and Scientific Research Publishing Inc.

Creative Commons License

This work and the related PDF file are licensed under a Creative Commons Attribution 4.0 International License.