Epileptic Seizure Detection & Classification Using MATLAB
DOI:
https://doi.org/10.18486/ijcsnt/8.1.100Keywords:
Brain, EEG, Epilepsy, MatlabAbstract
Epilepsy is that the tendency to possess seizures that begin within the brain. Basically brain takes electrical signals in use to pass messages between brain cells. If there is a discontinuity in signals, this may cause a seizure. Epilepsy is typically diagnosed once someone has had quite one seizure. These Seizures will have an effect on your feelings, awareness or movement. The manual recognition of given disorder by a medical specialist is pricey and time intense conjointly and there is also loss of precision for the reason of fatigue, computer based techniques etc. This work has projected a way that is very economical and offers correct results over EEG signals for encephalopathy. MATLAB is highly regarded and powerful tool for all-purpose system also commonly it is a broad purpose background for matrix based calculation and computation. The record of the electrical action of the brain from the scalp is referred to electro encephalogram. Electroencephalogram activity is sort of little, measured in micro volts with the main frequencies of interest up to or so 30 Hertz. Some parameters are used to classify EEG signal in Matlab. With the help of Matlab a program has been developed to visualize the situation of the signal whether or not it is healthy.
References
U. Orhan, M. Hekim, M. Ozer and I. Provaznik, “Epilepsy Diagnosis Using Probability Density Functions of EEG Signals,” Innovations in Intelligent Systems and Applications (INISTA), 2011 International Symposium, pp. 626–630, 2011. DOI: https://doi.org/10.1109/INISTA.2011.5946171
S. Bezobrazova and V. Golovko, “Comparative Analysis of Forecasting Neural Networks in the Application for Epilepsy Detection,” Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS 2007), 4th IEEE Workshop, pp. 202–206, 2007. DOI: https://doi.org/10.1109/IDAACS.2007.4488405
N. Sivasankari and K. Thanushkodi, “Epileptic Seizure Detection on EEG Signal Using Statistical Signal Processing and Neural Networks,” Proceedings of the 1st WSEAS International Conference on Sensors and Signals, pp. 98–102, 2008.
R. Panda, P. Khobragade, P. Jambhule, S. Jengthe, P. Pal and T. Gandhi, “Classification of EEG Signal Using Wavelet Transform and Support Vector Machine for Epileptic Seizure Detection,” Systems in Medicine and Biology (ICSMB), 2010 International Conference, pp. 405–408, 2010. DOI: https://doi.org/10.1109/ICSMB.2010.5735413
Y. Liu, Q. Zhou, Q. Yuan and S. Chen, “Automatic Seizure Detection Using Wavelet Transform and SVM in Long-Term Intracranial EEG,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 20, pp. 749–755, 2012. DOI: https://doi.org/10.1109/TNSRE.2012.2206054
L. D. Iasemidis, D. S. Shiau, W. Chaovalitwongse, J. C. Sackellares, P. N. Pardalos, J. C. Principe, P. R. Carney, A. Prasad, B. Veeramani and K. Tsakalis, “Adaptive Epileptic Seizure Prediction System,” IEEE Transactions on Biomedical Engineering, vol. 50, no. 5, pp. 616–627, May 2003. DOI: https://doi.org/10.1109/TBME.2003.810689
V. Srinivasan, C. Eswaran and N. Sriraam, “Artificial Neural Network Based Epileptic Detection Using Time-Domain and Frequency-Domain Features,” Journal of Medical Systems, vol. 29, no. 6, pp. 647–660, December 2005. DOI: https://doi.org/10.1007/s10916-005-6133-1
K. Polat and S. Güneş, “Classification of Epileptiform EEG Using a Hybrid System Based on Decision Tree Classifier and Fast Fourier Transform,” Applied Mathematics and Computation, vol. 187, no. 2, pp. 1017–1026, 2007. DOI: https://doi.org/10.1016/j.amc.2006.09.022
B. Boashash, M. Mesbah and P. Colditz, “Time-Frequency Detection of EEG Abnormalities,” in Time-Frequency Signal Analysis and Processing: A Comprehensive Reference, Missouri: Elsevier, 2003, ch. 15, article 15.5, pp. 663–670.
R. B. Pachori and P. Sircar, “EEG Signal Analysis Using FB Expansion and Second-Order Linear TVAR Process,” Signal Processing, vol. 88, pp. 415–420, 2008. DOI: https://doi.org/10.1016/j.sigpro.2007.07.022
H. Adeli, Z. Zhou and N. Dadmehr, “Analysis of EEG Records in an Epileptic Patient Using Wavelet Transform,” Journal of Neuroscience Methods, vol. 123, pp. 69–87, 2003. DOI: https://doi.org/10.1016/S0165-0270(02)00340-0
Y. U. Khan and J. Gotman, “Wavelet-Based Automatic Seizure Detection in Intracerebral Electroencephalogram,” Clinical Neurophysiology, vol. 114, pp. 898–908, 2003. DOI: https://doi.org/10.1016/S1388-2457(03)00035-X
A. T. Tzallas, M. G. Tsipouras and D. I. Fotiadis, “Automatic Seizure Detection Based on Time-Frequency Analysis and Artificial Neural Networks,” Computational Intelligence and Neuroscience, vol. 2007, p. 80510, 2007. DOI: https://doi.org/10.1155/2007/80510
R. Schuyler, A. White, K. Staley and K. J. Cios, “Epileptic Seizure Detection,” IEEE Engineering in Medicine and Biology Magazine, vol. 26, no. 2, pp. 74–81, March/April 2007. DOI: https://doi.org/10.1109/MEMB.2007.335592
S. G. Dastidar, H. Adeli and N. Dadmehr, “Mixed-Band Wavelet-Chaos-Neural Network Methodology for Epilepsy and Epileptic Seizure Detection,” IEEE Transactions on Biomedical Engineering, vol. 54, no. 9, pp. 1545–1551, September 2007. DOI: https://doi.org/10.1109/TBME.2007.891945
Varun Bajaj and Ram Bilas Pachori, “Classification of Seizure and Nonseizure EEG Signals Using Empirical Mode Decomposition,” IEEE Transactions on Information Technology in Biomedicine, vol. 16, no. 6, November 2012. DOI: https://doi.org/10.1109/TITB.2011.2181403
Neep Hazarika, Ah Chung Tsoi and Alex A. Sergejew, “Nonlinear Considerations in EEG Signal Classification,” IEEE Transactions on Signal Processing, vol. 45, no. 4, April 1997. DOI: https://doi.org/10.1109/78.564171
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