Epilepsy is one of the most prevalent neurological disorders with no age, racial, social class, and neither national nor geographic boundaries. There are 50 million sufferers in the world today with 2.4 million new cases occur each year. Electroencephalogram (EEG) has become a traditional procedure to investigate abnormal functioning of brain activity. Epileptic EEG is usually characterized by short transients and sharp waves as spikes. Identification of such event splays a crucial role in epilepsy diagnosis and treatment. The present study proposes a method to detect three epileptic spike types in EEG recordings based mainly on Template Matching Algorithm including multiple signal-processing approaches. The method was applied to real clinical EEG data of epileptic patients and evaluated according to sensitivity, specificity, selectivity and average detection rate. The promising results illuminate that hybrid processing approaches in temporal, frequency and spatial domains can be a real solution to identify fast EEG transients.
Longo, D.L (2012) 369 Seizures and Epilepsy Harrison’s Principles of Internal Medicine. 18th Edition, McGraw-Hill, New York, 3258.
Lerche, H., Shah, M., Beck, H., Noebels, J., Johnston, D. and Vincent, A. (2012) Ion Channels in Genetic and Acquired Forms of Epilepsy. The Journal of Physiology, 591, 753-764. Published Online 2012 October 22. https://doi.org/10.1113/jphysiol.2012.240606
Acir, N., Oztura, I., Kuntalp, M., Balkan, B. and Guzelis, C. (2005) Automatic Detection of Epileptiform Events in EEG by a Three-Stage Procedure Based on Artificial Neural Networks. IEEE Transactions on Biomedical Engineering, 52, 30-40. https://doi.org/10.1109/TBME.2004.839630
Radmehr, M. and Anisheh, S.M. (2013) EEG Spike Detection Using Stationary Wavelet Transform and Time-Varying Autoregressive Model. International Journal of Computer Applications, 83, 3 p. https://doi.org/10.5120/14505-2117
Salam, M.T., Sawan, M. and Nguyen, D.K. (2011) A Novel Low-Power-Implantable Epileptic Seizure-Onset Detector. IEEE Transactions on Biomedical Circuits and Systems, 5, 568-578.
Yadav, R., Swamy, M.N.S. and Agarwal, R. (2012) Model-Based Seizure Detection for Intracranial EEG Recordings. IEEE Transactions on Biomedical Engineering, 59, 1419-1428.
Yadav, R., Shah, A.K., Loeb, J.A., Swamy, M.N.S. and Agarwal, R. (2012) Morphology-Based Automatic Seizure Detector for Intracerebral EEG Recordings. IEEE Transactions on Biomedical Engineering, 59, 1871-1881. https://doi.org/10.1109/TBME.2012.2190601
Yadav, R., Agarwal, R. and Swamy, M.N.S. (2007) Detection of Epileptic Seizures in Stereo-EEG Using Frequency-Weighted Energy. Circuits and Systems, 50th Midwest Symposium, MWSCAS 2007, Montreal, 5 -8 August 2007, 77-80. https://doi.org/10.1109/mwscas.2007.4488544
Yadav, R., Agarwal, R. and Swamy, M.N.S. (2008) A Novel Dual-Stage Classifier for Automatic Detection of Epileptic Seizures. 30th Annual International Conference of the Engineering in Medicine and Biology Society, Vancouver, 20-25 August 2008, 911-914. https://doi.org/10.1109/iembs.2008.4649302
Yadav, R., Agarwal, R. and Swamy, M.N.S. (2009) A New Improved Model-Based Seizure Detection Using Statistically Optimal Null Filter. Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2009, 1318-1322. https://doi.org/10.1109/iembs.2009.5334138
Yadav, R., Agarwal, R. and Swamy, M.N.S. (2010) A Novel Morphology-Based Classifier for Automatic Detection of Epileptic Seizures. International Conference of the IEEE Engineering in Medicine and Biology Society, Buenos Aires, 31 August-4 September 2010, 5545-5548. https://doi.org/10.1109/iembs.2010.5626781
Yadav, R., Shah, A.K., Loeb, J.A., Swamy, M.N.S. and Agarwal, R. (2011) A Novel Unsupervised Spike Sorting Algorithm for Intracranial EEG. 33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Boston, 30 August-3 September 2011, 7545-7548.
Oikonomou, V.P., Tzallas, A.T. and Fotiadis, D.I. (2007) A Kalman Filter Based Methodology for EEG Spike Enhancement. Computer Methods and Programs in Biomedicine, 85, 101-108. https://doi.org/10.1016/j.cmpb.2006.10.003
Tzallas, A.T., Tsipouras, M.G., Tsalikakis, D.G., Karvounis, E.C., Astrakas, L., Konitsiotis, S. and Tzaphlidou, M. (2012) Automated Epileptic Seizure Detection Methods: A Review Study. In: Stevanovic, D., Ed., Epilepsy-Histological, Electroencephalographic and Psychological Aspects, InTech. http://www.intechopen.com/books/epilepsy-histological-electroencephalographic-and-psychological-aspects/ automated-epileptic-seizure-detection-methods-a-review-study
Kamath, C. (2015). Analysis of EEG Dynamics in Epileptic Patients and Healthy Subjects Using Hilbert Transform Scatter Plots. Open Access Library Journal, 2, e745. https://doi.org/10.4236/oalib.1100745
Orosco, L., Correa, A. and Laciar, E. (2013). Review: A Survey of Performance and Techniques for Automatic Epilepsy Detection. Journal of Medical and Biological Engineering, 33, 526-537. https://doi.org/10.5405/jmbe.1463
Seneviratne, U., Cook, M. and D’Souza, W. (2012) The Electroencephalogram of Idiopathic Generalized Epilepsy. Epilepsia, 53, 234-248. https://doi.org/10.1111/j.1528-1167.2011.03344.x
GopikaGopan, K., Harsha, A., Joseph, L.A. and Kollialil, E.S. (2013) Adaptive Neuro-Fuzzy Classifier for “Petit Mal” Epilepsy Detection Using Mean Teager Energy. International Conference on Advances in Computing, Communications and Informatics, Mysore, 22-25 August 2013, 752-757.
Markand, O.N. (2003) Pearls, Perils, and Pitfalls in the Use of the Electroencephalogram. Seminars in Neurology, 23, 7-46. https://doi.org/10.1055/s-2003-40750
Pang, C.C.C., Upton, A., Shine, C. and Kamath, M. (2003) A Comparison of Algorithms for Detection of Spikes in the Electroencephalogram. IEEE Transactions on Biomedical Engineering, 50, 521-525. https://doi.org/10.1109/TBME.2003.809479
Hyvarinen, A., Karhunen, J. and Oja, P. (2001) Independent Component Analysis. John Wiley and Sons, Hoboken. https://doi.org/10.1002/0471221317
Comon, P. and Jutten, C. (2009) Handbook of Blind Source Separation of Independent Component Analysis and Applications. Academic Press, Cambridge.
Ossadtchi, A., Baillet, S., Mosher, J.C., Thyerlei, D., Sutherling, W. and Leahy, R.M. (2004) Automated Interictal Spike Detection and Source Localization in Magnetoencephalography Using Independent Components Analysis and Spatiotemporal Clustering. Clinical Neurophysiology, 115, 508-522. https://doi.org/10.1016/j.clinph.2003.10.036
Kobayashi, K., James, C.J., Nakahori, T., Akiyama, T. and Gotman, J. (1999) Isolation of Epileptiform Discharges from Unaveraged EEG by Independent Component Analysis. Clinical Neurophysiology, 110, 1755-1763. https://doi.org/10.1016/S1388-2457(99)00134-0
Kobayashi, K., Merlet, I. and Gotman, J. (2001) Separation of Spikes from Background by Independent Component Analysis with Dipole Modeling and Comparison to Intracranial Recording. Clinical Neurophysiology, 112, 405-413. https://doi.org/10.1016/S1388-2457(01)00457-6
Kobayashi, K., Akiyama, T., Nakahori, T., Yoshinaga, H. and Gotman, J. (2002) Systematic Source Estimation of Spikes by a Combination of Independent Component Analysis and RAP-MUSIC. II: Preliminary Clinical Application. Clinical Neurophysiology, 113, 725-734. https://doi.org/10.1016/S1388-2457(02)00047-0
Onton, J., Westerfield, M., Townsend, J. and Makeig, S. (2006) Review Imaging Human EEG Dynamics Using Independent Component Analysis. Neuroscience and Biobehavioral Reviews, 30, 808-822. https://doi.org/10.1016/j.neubiorev.2006.06.007
Walters-Williams, J. and Li, Y. (2011) A New Approach to Denoising EEG Signals—Merger of Translation Invariant Wavelet and ICA. International Journal of Biometric and Bioinformatics, 5, 130-148.
Song, Y. and Zhang, J. (2013) Automatic Recognition of Epileptic EEG Patterns via Extreme Learning Machine and Multiresolution Feature Extraction. Expert Systems with Applications, 40, 5477-5489. https://doi.org/10.1016/j.eswa.2013.04.025
Indiradevi, K.P., Elias, E., Sathidevi, P.S., Dinesh Nayak, S. and Radhakrishnan, K. (2008) A Multi-Level Wavelet Approach for Automatic Detection of Epileptic Spikes in the Electroencephalogram. Computers in Biology and Medicine, 38, 805-816. https://doi.org/10.1016/j.compbiomed.2008.04.010
Halford, J., Schalkoff, R. and Zhou, J. (2013) Standardized Database Development for EEG Epileptiform Transient Detection: EEGnet Scoring System and Machine Learning Analysis. Journal of Neuroscience, 212, 308-316. https://doi.org/10.1016/j.jneumeth.2012.11.005
Suresh, H.N. and Balasubramanyam, V. (2012) Wavelet Transforms and Neural Network Approach for Epileptical EEG. 12-17.
Jahankhani, P., Kodogiannis, V. and Revett, K. (2006) EEG Signal Classification Using Wavelet Feature Extraction and Neural Networks. IEEE John Vincent Atanasoff 2006 International Symposium on Modern Computing, Sofia, 3-6 October 2006, 120-124. https://doi.org/10.1109/JVA.2006.17
Subasi, A. (2007) EEG Signal Classification Using Wavelet Feature Extraction and a Mixture of Expert Model. Expert Systems with Applications, 32, 1084-1093. https://doi.org/10.1016/j.eswa.2006.02.005
Guo, L., Rivero, D., Dorado, J., Rabunal, J.R. and Pazos, A. (2010) Automatic Epileptic Seizure Detection in EEG Based on Line Length Feature and Artificial Neural Network. Journal of Neuroscience Methods, 191, 101-109. https://doi.org/10.1016/j.jneumeth.2010.05.020
Esteller, R., Echauz, J. and Tcheng, T. (2004) Comparison of Line Length Feature before and after Brain Electrical Stimulation in Epileptic Patients. 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2, 4710-4713. https://doi.org/10.1109/IEMBS.2004.1404304
Quiroga, R.Q. (1998) Quantitative Analysis of EEG Signals: Time-Frequency Methods and Chaos Theory. Institute of Physiology-Medical University Lubeck and Institute of Signal Processing-Medical University Lubeck.
Saini, B.S., Singh, D., Uddin, M. and Kumar, V. (2008) Improved Power Spectrum Estimation for RR-Interval Time Series. World Academy of Science, Engineering and Technology, 46, 44-48.
Li, M., Cui, Y. and Yang, J. (2013) Automatic Removal of Ocular Artifact from EEG with DWT and ICA Method. Applied Mathematics & Information Sciences, 7, 809-816. https://doi.org/10.12785/amis/070252
Manzo-Martínez, A. and Camarena-Ibarrola, J. (2013) A New and Efficient Alignment Technique by Cosine Distance. International Journal of Combinatorial Optimization Problems and Informatics, 4, 12-24.