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Automatyczna detekcja ataku epilepsji przy wykorzystaniu połączenia różnych metod: falkowej - klasyfikacji KNN i wzajemnej informacji
Języki publikacji
Abstrakty
Electroencephalogram (EEG) is the brain signal that contains the valuable information about different states of the brain. In this study EEG signals are analyzed for evaluating epileptic seizures in these signals and their sub-bands and comparing epileptic states with other states. A discrete wavelet transform is applied for decompose the EEGs into its sub-bands. The chaotic behavior of EEGs is evaluated by means ol normalized Shannon and spectral entropies. Entropy method is presented for detection of epileptic seizures through the analysis of EEGs and their sub-bands. At the end the mixture K-nearest neighbor and mutual information method is applied as a classifier to classify the different states in EEGs and their sub-bands. This method is applied to three different groups of EEG signals: 1) healthy states, 2) epileptic states during a seizure-free interval (interictal EEG), 3) epileptic states during a seizure (ictal EEG). The proposed method could classify different states with 99% accuracy.
Elektroencefalografia EEG jest analizą sygnału mózgu. W artykule przedstawiono metody analizy sygnału EEG stosowane w celu wykrycia epilepsji. Zastosowano dyskretną transformatę falkową do dekompozycji sygnału EEG. Wykorzystano metodę entropii do detekcji sygnału związanego z epilepsją. Metody zastosowano do trzech grup pacjentów: zdrowych, chorych na epilepsję i chorych w czasie ataku epilepsji.
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Wydawca
Czasopismo
Rocznik
Tom
Strony
220--223
Opis fizyczny
Bibliogr. 13 poz., rys., tab.
Twórcy
Bibliografia
- [1] H. Witte, L. D. Iasemidis, and B. Litt, "Special isse on epileptic seizures prediction," IEEE Trans. Biomed. Eng., vol. 50, no. 5, pp. 537-539, May 2003.
- [2] L. D. Iasemidis and J. C. Sackellares, "The temporal evolution of the largest Lyapunov exponent on the human epileptic cortex," in Measuring Chaos in the Human Brain, D. W. Duke and W. S. Pritchard, Eds. Singapore: World Scientific, 1991, pp. 49-82.
- [3] L. D. Lasemidis, D. S. Shiau, J. C. Sackellares, and P. M. Pardalos, "Transition to epileptic seizures: Optimization," in DIMACS Series in Discrete Mathematics and Theoretical Computer Science. Providence, Rl: American Mathematical Society, 2000, vol. 55, pp. 55-74.
- [4] L. D. Iasemidis, D. S. Shiau, W. Chaovalitwongse, J. C. Sackellares, P. M. Pardalos, J. C. Principle, P. R. Carney, A. Prasad, B. Veeramani, and K. Tsakalis, "Adaptive epileptic seizure prediction system," IEEE Trans. Biomed. Eng., vol. 50, no. 5, pp. 616-627, May 2003.
- [5] H. Adeli, S. Ghosh-Dastidar, and N. Dadmehr, "A wavelet-chaos methodology for analysis of EEGs and EEG sub-bands to detect seizure and epilepsy, "IEEE Trans. Biomed. Eng, vol. 54, no. 2, pp. 205-211, Feb. 2007.
- [6] H. Adeli, Z. Zhou, and N. Dadmehr, "Analysis of EEG records in an epileptic patient using wavelet transform," J. Neurosci. Meth, vol. 123, pp. 69-87, 2003.
- (7] S. Ghosh-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, September 2007.
- [8] S. P. Kumar, N. Sriraam and P. G. Benakop, "Automated Detection of Epileptic Seizures Using Wavelet Entropy Feature with Recurrent Neural Network Classifier," TENCON IEEE, pp. 1-5.2008.
- [9] H. Ocak, " Automatic detection of epileptic seizures in EEG using discrete wavelet transform and approximate entropy," Elsevier Ltd, vol. 36. Pp. 2027-2036. 2009.
- [10] R. G. Andrzejak, K. Lehnertz, C. Rieke, F. Mormann, P. David, and C. E. Elger, Indications of nonlinear deterministic and finite-dimensional structures in time series of brain electrical activity: Dependence on recording region and brain state, Physical Review E, vol. 64, 061907, Nov. 2001. http://www.meb.uni-bonn.de/epileptologie/science/physik/eegdata.html.
- [11] C. E. Shannon, A mathematical theory of communication, Bell Syst. Tech. J. 27(1948) 379-423.
- [12] R. Ferenets, T. Lipping, A. Anier, V. Jantti, S.Melto, and S. Hovilehto, Comparison of Entropy and Complexity Measures for the Assessment of Depth of Sedation, IEEE Transactions on Biomedical Engineering, vol. 53, No. 6, June 2006.
- [13] H. Peng, F. Long, and C. Ding. "Feature selection based on mutual information: criteria of max-dependency, max-relevance, and min-redundancy," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 27, No. 8, pp, 1226-1238, 2005.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BPS1-0044-0092