LDASpike for Recognizing Epileptic Spikes in EEG

LDASpike for Recognizing Epileptic Spikes in EEG

Автор: Anup Kumar Keshri, Aishwarya Singh, Barda Nand Das, Rakesh Kumar Sinha

Журнал: International Journal of Information Engineering and Electronic Business(IJIEEB) @ijieeb

Статья в выпуске: 4 vol.5, 2013 года.

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Manual processing of recorded EEG data for characteristics like epileptic spikes is very time consuming since the recording of EEG for a longer duration producing enormous amount of data. Therefore, automated systems are required to speed up the processing. In the current work, a classification method has been proposed for detecting the epileptic spikes in the recorded EEG data by using Linear Discriminant Analysis (LDA) and has been named LDASpike. The prerecorded EEG data files were used as input to LDASpike and the output produced was the total number of spikes present in each EEG file. The proposed method results on an average sensitivity 100% and selectivity 95.38%, when the training and testing data were same. However, with four fold cross-validation applied in this work, the sensitivity and selectivity were achieved as 98.45% and 96.06%, respectively, on an average. Though a little time initially is spent to train the system but the result produced by the system is very promising and can be compared with the existing standard methods. This system can also works with the real time recording and processing for a clinical setup.

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Automated classification, Electroencephalogram, Epileptic Spikes, Linear Discriminant Analysis

Короткий адрес: https://sciup.org/15013196

IDR: 15013196

Список литературы LDASpike for Recognizing Epileptic Spikes in EEG

  • Ficker, D. M., Sudden unexplained death and injury in epilepsy. Epilepsia. 41(Suppl 2): S7–12, 2000.
  • Sperling, M. R., Sudden Unexplained Death in Epilepsy. Epilepsy Currents. 1: 21-23, 2001.
  • Gotman, J., and Gloor, P., Automatic recognition and quantification of interictal epileptic activity in the human scalp EEG. Electroencephalogr Clin Neurophysiol. 41:513–529, 1976.
  • Gotman, J., Ives J. R., and Gloor, P., Automatic recognition of interictal epileptic activity in prolonged EEG recordings. Electroencephalogr Clin Neurophysiol. 46:510–20, 1979.
  • Gotman, J., Flanagah, D., Zhang, J., and Rosenblatt, B., Automatic seizure detection in the newborn: Methods and initial evaluation. Electroencephalogr. Clin. Neurophysiol. 103:356-362 1997.
  • Inan, Z. H., and Kuntalp, M., A study on fuzzy C-means clustering- based systems in automatic spike detection. Comput. Biol. Med. 37:1160-1166, 2007.
  • Liu, B., and Echauz, J., Prediction of epileptic seizures. Lancet Neurol. 1:22-30, 2002.
  • Subasi, A., EEG signal classification using wavelet feature extraction and a mixture of expert model. Expert Systems with Appications. 32:1084 – 1093, 2007.
  • Indiradevi, K. P., Elias, E., Sathidevi, P. S., Nayak, S. D., and Radhakrishnan, K., A multi – level wavelet approach for automatic detection of epileptic spikes in the electroencephalogram. Comput. Biol. Med. 38: 805 – 816, 2008.
  • Nenadic, Z., and Burdick, J. W., Spike detection using the continuous wavelet transform. IEEE Trans. BME. 52:74-87, 2009.
  • Ocak, H., Automatic detection of epileptic seizures in EEG using discrete wavelet transforms and approximate entropy. Expert Systems with Applications. 36:2027 – 2036, 2009.
  • Kiymik, M. K., Akin, M., and Subasi, A., Automated recognition of alertness level by using wavelet transform and artificial neural network. J. Neurosci. Methods. 139:231 – 240, 2004.
  • Sinha, R. K., Backpropogation artificial neural network to detect hyperthermic seizures in rats. Online J. Health Allied Sci. 4:1, 2002.
  • Petrosian, A., Prokhorov, D., Homan, R., Dashei, R., and Wunsch, D., Recurrent neural network based prediction of epileptic seizures in intra- and extracranial EEG. Neurocomputing. 30:201-218, 2000.
  • Sinha, R. K., Ray, A. K., and Agarwal, N. K., An artificial neural network to detect EEG seizures. Neurol. India. 52:399-400, 2004.
  • Webber, W. R. S., Lesser, R. P., Richardson, R. T., and Wilson, K., An approach to seizure detection using an artificial neural network (ANN), Electroencephalogr. Clin. Neurophysiol. 98:250-272, 1996.
  • Kocer, S., and Canal, M. R., Classifying epilepsy diseases using artificial neural networks and genetic algorithm. J. Med. Syst. 35:489-498, 2011.
  • Keshri, A. K., Sinha, R. K., Hatwal, R., and Das, B. N., Epileptic spike recognition in electroencephalogram using deterministic finite automata. J. Med. Syst. 33:173-179, 2009.
  • Keshri, A. K., Sinha, R. K., Mallick, D. K., and Das, B. N., Parallel algorithm to analyze the brain signals: Application on epileptic spikes. J. Med. Syst. 35:93-104, 2011.
  • Keshri, A. K., Sinha, R. K., Singh, A., and Das, B. N., DFASpike: A new computational proposition for efficient recognition of epileptic spike in EEG. Comput. Biol. Med. 41:559 – 564, 2011.
  • Lewis, R. A., Shmueli, D., and White, A. M., Deterministic Finite Automata in the Detection of EEG Spikes and Seizures. Advances in Intelligent Data Analysis. 6065: 103-113, 2010.
  • Lewis, R. A., Parks, B., Shmueli, D., and White, A. M., Deterministic finite automata in the detection of epileptogenesis in a noisy domain. International Conference Intelligent Information Systems (IIS), in Proceedings of the Joint venture of the 18th International Conference Intelligent Information Systems (IIS) and the 25th International Conference on Matricidal Intelligence, Siedlce. Poland. 207–218, 2010
  • Zhanfeng, J., Sugi, T., Goto, S., Xingyu, W., Ikeda, A., Nagamine, T., Shibasaki, H., and Nakamura, M., An Automatic Spike Detection System Based on Elimination of False Positives Using the Large-Area Context in the Scalp EEG. IEEE Trans. BME. 58:2478-2488, 2011.
  • Garrett, D., Peterson, D., Anderson, C., and Thaut, M., Comparison of linear and nonlinear methods for EEG signal classi?cation. IEEE Trans.Neural Sys.Rehab. Eng. 11:141-144, 2003.
  • Guo, L., Rivero, D., Dorado, J., Rabu?al, J. R., and Pazos, A., Automatic epileptic seizure detection in EEGs based on line length feature and artificial neural networks. J. Neurosci. Meth. 15:101-109, 2010.
  • Bermejo-Barrera, P., Moreda-Pi?eiro, A., Moreda-Pi?eiro, J., Bermejo-Barrera, A., and Bermejo-Barrera, A. M., A study of illicit cocaine seizure classification by pattern recognition techniques applied to metal data. J. Forensic Sci. 44:270-274, 1999.
  • Ghosh-Dastidar, S., Adeli, H., and Dadmehr, N., Mixed-band wavelet-chaos-neural network methodology for epilepsy and epileptic seizure detection. IEEE Trans. BME. 54:1545-1551, 2007.
  • Subasi, A., and Gursoy, M. I., EEG signal classification using PCA, ICA, LDA and support vector machines. Exp. Sys. App. 37: 8659-8666, 2010.
  • Sinha, R. K., Artificial Neural Network detects changes in electro-encephalogram power spectrum of different sleep-wake states in an animal model of heat stress. Med. Biol. Eng. Comput. 41:595-600, 2003.
  • Sinha, R. K., Electro-encephalogram disturbances in different sleep-wake states following exposure to high environmental heat. Med. Biol. Eng. Comput. 42:282-287, 2004.
  • Duda, R. O., Hart, P. E., and Stork, D. G., Pattern Classification. Wiley 2nd Edition. 2001.
  • Schalkoff, R. J., Pattern Recognition: Statistical, Structural and Neural Approaches. Wiley India Edition. 2007.
  • Press, W. H., Teukolsky, S. A., Vetterling, W. T., and Flannery, B. P., Numerical Recipes: The Art of Scientific Computing. Cambridge University Press, 3rd Edition. 2007.
  • Han, J., and Kamber, M., Data Mining: Concepts and Techniques. 2nd Edition, 2006.
  • Black, M. A., and Jones, R. D., Sensitivity and selectivity for continuous perception values – a comment. Electroencephalography and clinical Neurophysiology. 106: 457–459, 1998.
  • Wilson, S. B., Harner, R. N., Duffy, F. H., Tharp, B. R., Nuwer, M. R. and Sperling, M. R., Spike detection. I. Correlation and reliability of human experts. Electroenceph. clin. Neurophysiol. 98:186–198, 1996.
  • Firpi, H., Goodman, E. D., and Echauz, J., Epileptic seizure detection using genetically programmed artificial features. IEEE Trans. BME. 54:212-224, 2007.
  • Goelz, H., Jones, R. D., and Bones, P. J., Wavelet analysis of transient biomedical signals and its application to detection of epileptiform activity in the EEG. Clin Electroencephalogr. 31:181–191, 2000.
  • Witte, H., Eiselt, M., Patakova, I., Petranek, S., Griessbach, G., Krajca, V., and Rother, M., Use of discrete Hilbert transformation for automatic spike mapping: a methodological investigation. Med Biol Eng Comput. 29:242–248, 1991.
  • Gabor, A. J., and Seyal, M., Automated interictal EEG spike detection using artificial neural networks. Electroenceph clin Neurophysiol. 83:271–280, 1992.
  • Kutlu, Y., Kuntalp, M., and Kuntalp, D., Optimizing the performance of an MLP classifier for the automatic detection of epileptic spikes. Expert Systems with Applications. 36: 7567–7575, 2009.
  • Agarwal, R., and Gotman, J., Computer-Assisted Sleep Staging. IEEE Trans. BME . 48:12:1412 – 1423, 2001.
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