ANN-based classification of EEG signals using the average power based on rectangle approximation window;Klasyfikacja sygnału eeg wykorzystująca sieci neuronowe oraz uśrednioną moc w oknie prostokątnym

Przeglad Elektrotechniczny

Volumn 88, Issue 8, 2012, Pages 210-215

  Hekim, Mahmut ^a  

^a GAZIOSMANPASA UNIVERSITY   (Turkey)

Author keywords

Artificial neural networks; Discrete wavelet transform (DWT); EEG signals; Power spectral density (PSD); Rectangle approximation window based average power

Indexed keywords

EID: 85061723691     PISSN: 00332097     EISSN: 24499544     Source Type: Journal    
DOI: None     Document Type: Article

References (28)

1. Acharya U.R., Molinari F., Sree S.V., Chattopadhyay S., Ng K. H., Suri J.S., Automated diagnosis of epileptic EEG using entropies, Biomedical Signal Processing and Control, doi: 10.1016/j.bspc. 2011.07.007, 2011
2. Pravin Kumar S., Sriraam N., Benakop P.G., Jinaga B.C., Entropies based detection of epileptic seizures with artificial neural network classifiers, Expert Systems with Applications, 37 (2010), 3284–3291
3. Naghsh-Nilchi A.R., Aghashahi M., Epilepsy seizure detection using eigen-system spectral estimation and multiple layer perceptron neural network, Biomedical Signal Processing and Control, 5 (2010), 147–157
4. Ubeyli E.D., Combined neural network model employing wavelet coefficients for EEG signals classification, Digital Signal Processing, 19 (2009), 297–308
5. Aarabi A., Fazel-Rezai R., Aghakhani Y., A fuzzy rule-based system for epileptic seizure detection in intracranial EEG, Clinical Neurophysiology, 120 (2009), 1648–1657
6. Adeli H., Zhou Z., Dadmehr N., Analysis of EEG records in an epileptic patient using wavelet transform, Journal of Neuroscience Methods, 123 (2003), 69–87
7. Subasi A., Automatic recognition of alertness level from EEG by using neural network and wavelet coefficients, Expert Systems with Applications, 28 (2005), 701–711
8. Sun S., Zhang C., Zhan D., An experimental evaluation of ensemble methods for EEG signal classification, Pattern Recognition Letters, 28 (15) (2007), 2157-2163
9. Guler I., Ubeyli E.D., Adaptive neuro-fuzzy inference system for classification of EEG signals using wavelet coefficients, Journal of Neuroscience Methods, 148 (2005), 113–121
10. Orhan U., Hekim M., Ozer M., EEG signals classification using the K-means clustering and a multilayer perceptron neural network model, Expert Systems with Applications, 38 (2011), 13475–13481
11. Orhan U., Hekim M., Ozer M., Epileptic seizure detection using probability distribution based on equal frequency discretization, Journal of Medical Systems doi: 10.1007/s10916-011-9689-y, 2011
12. Orhan U., Hekim M., Ozer M., Epileptic seizure detection using artificial neural network and a new feature extraction approach based on equal width discretization, Journal of the Faculty of Engineering and Architecture of Gazi University, 26 (3) (2011), 575-580
13. Ocak H., Optimal classification of epileptic seizures in EEG using wavelet analysis and genetic algorithm, Signal Processing, 88 (2008), 1858–1867
14. Subasi A., EEG signal classification using wavelet feature extraction and a mixture of expert model, Expert Systems with Applications, 32 (2007), 1084–1093
15. Subasi A., Application of adaptive neuro-fuzzy inference system for epileptic seizure detection using wavelet feature extraction, Computers in Biology and Medicine, 37 (2), (2007) 227–244
16. Srinivasan V., Eswaran C., Sriraam N., Artificial neural network based epileptic detection using time-domain and frequency-frequency- domain features, Journal of Medical Systems, 29 (6) (2005), 647–660 domain
17. Srinivasan V., Eswaran C., Sriraam N., Approximate entropy-based epileptic EEG detection using artificial neural networks, IEEE Transactions on Information Technology in Biomedicine, 11 (3) (2007), 288–295
18. Hazarika N., Chen J.Z., Tsoi A.C., Sergejew A., Classification of EEG signals using the wavelet transform, Signal Processing, 59 (1) (1997), 61–72
19. Kocyigit Y., Alkan A., Erol H., Classification of EEG recordings by using fast independent component analysis and artificial neural network, Journal of Medical Systems, 32 (2008), 17-20
20. Alkan A., Koklukaya E., Subasi A., Automatic seizure detection in EEG using logistic regression and artificial neural network, Journal of Neuroscience Methods, 148 (2005), 167–176
21. Subasi A., Ercelebi E., Classification of EEG signals using neural network and logistic regression, Computer Methods and Programs in Biomedicine, 78 (2005), 87–99
22. Kiymik M.K., Akin M., Subasi A., Automatic recognition of alertness level by using wavelet transform and artificial neural network, Journal of Neuroscience Methods, 139 (2004), 231– 240
23. Kiymik M.K., Subasi A., Ozcalik H.R., Neural networks with periodogram and autoregressive spectral analysis methods in detection of epileptic seizure, Journal of Medical Systems, 28 (6) (2004), 511–522
24. Roura J.C., Romeral-Martinez J.L., Transient analysis and motor fault detection using the wavelet transform, Discrete Wavelet Transforms-Theory and Applications, Juuso T. Olkkonen (Ed.), 2011.
25. Delgado-Saa J.F., Gutierrez M.S., EEG signal classification using power spectral features and linear discriminant analysis: a brain computer interface application, Proceedings of the Eighth LACCEI Latin American and Caribbean Conference for Engineering and Technology, 2010.
26. Andrzejak R.G., Lehnertz K., Mormann F., Rieke C., David P., Elger C.E., 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, 64 (6) (2001), 061907
27. Gonzalez R.C., Woods R.E., Digital Image Processing, Second Edition, Prentice Hall, 2002.
28. Jang J.S.R., ANFIS: Adaptive-network based fuzzy inference systems, IEEE Transaction on Systems, Man, and Cybernetics, 23 (03) (1993), 665-685