Statistics over features: EEG signals analysis

Computers in Biology and Medicine

Volumn 39, Issue 8, 2009, Pages 733-741

  Derya Übeyli, Elif ^a  

^a TOBB UNIVERSITY OF ECONOMICS AND TECHNOLOGY   (Turkey)

Author keywords

Eigenvector methods; Electroencephalogram (EEG) signals; Feature extraction selection; Lyapunov exponents; Wavelet coefficients

Indexed keywords

EIGENVECTOR METHODS; ELECTROENCEPHALOGRAM (EEG) SIGNALS; FEATURE EXTRACTION/SELECTION; LYAPUNOV EXPONENTS; WAVELET COEFFICIENTS;

DIFFERENTIAL EQUATIONS; EIGENVALUES AND EIGENFUNCTIONS; ELECTROENCEPHALOGRAPHY; FEATURE EXTRACTION; LYAPUNOV METHODS; NEURAL NETWORKS; OPTICAL SENSORS; POWER SPECTRAL DENSITY; WAVELET TRANSFORMS;

SIGNAL DETECTION;

ALGORITHM; ARTICLE; ARTIFICIAL NEURAL NETWORK; CLASSIFICATION; CONTROLLED STUDY; DECISION SUPPORT SYSTEM; ELECTROENCEPHALOGRAM; EPILEPSY; EPILEPTIC DISCHARGE; HUMAN; PRIORITY JOURNAL; SEIZURE; SPECTROSCOPY; STATISTICS; VOLUNTEER;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; COMPUTERS; ELECTROENCEPHALOGRAPHY; EPILEPSY; HUMANS; MODELS, STATISTICAL; PATTERN RECOGNITION, AUTOMATED; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY; SIGNAL PROCESSING, COMPUTER-ASSISTED; SOFTWARE;

EID: 67649601026     PISSN: 00104825     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.compbiomed.2009.06.001     Document Type: Article

References (35)

1. Lindenberg A.M. The evolution of complexity in human brain development: an EEG study. Electroencephalography and Clinical Neurophysiology 99 5 (1996) 405-411
2. Agarwal R., Gotman J., Flanagan D., and Rosenblatt B. Automatic EEG analysis during long-term monitoring in the ICU. Electroencephalography and Clinical Neurophysiology 107 1 (1998) 44-58
3. Adeli H., Zhou Z., and Dadmehr N. Analysis of EEG records in an epileptic patient using wavelet transform. Journal of Neuroscience Methods 123 1 (2003) 69-87
4. Hazarika N., Chen J.Z., Tsoi A.C., and Sergejew A. Classification of EEG signals using the wavelet transform. Signal Processing 59 1 (1997) 61-72
5. Güler N.F., Übeyli E.D., and Güler I. Recurrent neural networks employing Lyapunov exponents for EEG signals classification. Expert Systems with Applications 29 3 (2005) 506-514
6. Güler I., and Übeyli E.D. Adaptive neuro-fuzzy inference system for classification of EEG signals using wavelet coefficients. Journal of Neuroscience Methods 148 2 (2005) 113-121
7. Übeyli E.D. Analysis of EEG signals using Lyapunov exponents. Neural Network World 16 3 (2006) 257-273
8. Übeyli E.D., and Güler I. Features extracted by eigenvector methods for detecting variability of EEG signals. Pattern Recognition Letters 28 5 (2007) 592-603
9. Güler I., and Übeyli E.D. Multiclass support vector machines for EEG signals classification. IEEE Transactions on Information Technology in Biomedicine 11 2 (2007) 117-126
10. Übeyli E.D. Wavelet/mixture of experts network structure for EEG signals classification. Expert Systems with Applications 34 3 (2008) 1954-1962
11. Übeyli E.D. Analysis of EEG signals by combining eigenvector methods and multiclass support vector machines. Computers in Biology and Medicine 38 1 (2008) 14-22
12. Übeyli E.D. Implementing eigenvector methods/probabilistic neural networks for analysis of EEG signals. Neural Networks 21 9 (2008) 1410-1417
13. Übeyli E.D. Analysis of EEG signals by implementing eigenvector methods/recurrent neural networks. Digital Signal Processing 19 1 (2009) 134-143
14. Übeyli E.D. Decision support systems for time-varying biomedical signals: EEG signals classification. Expert Systems with Applications 36 2 (2009) 2275-2284
15. Übeyli E.D. Combined neural network model employing wavelet coefficients for EEG signals classification. Digital Signal Processing 19 2 (2009) 297-308
16. Übeyli E.D. Probabilistic neural networks combined with wavelet coefficients for analysis of EEG signals. Expert Systems 26 2 (2009) 147-159
17. E.D. Übeyli, Modified mixture of experts employing eigenvector methods and Lyapunov exponents for analysis of EEG signals, Expert Systems (2009), in press.
18. Nigam V.P., and Graupe D. A neural-network-based detection of epilepsy. Neurological Research 26 1 (2004) 55-60
19. Lehnertz K. Non-linear time series analysis of intracranial EEG recordings in patients with epilepsy-an overview. International Journal of Psychophysiology 34 1 (1999) 45-52
20. Pijn J.P., Neerven J.V., Noest A., and Lopes da Silva F.H. Chaos or noise in EEG signals; dependence on state and brain site. Electroencephalography and Clinical Neurophysiology 79 5 (1991) 371-381
21. Silipo R., Deco G., Vergassola R., and Bartsch H. Dynamics extraction in multivariate biomedical time series. Biological Cybernetics 79 1 (1998) 15-27
22. Daubechies I. The wavelet transform, time-frequency localization and signal analysis. IEEE Transactions on Information Theory 36 5 (1990) 961-1005
23. Soltani S. On the use of the wavelet decomposition for time series prediction. Neurocomputing 48 (2002) 267-277
24. Akay M. Wavelet applications in medicine. IEEE Spectrum 34 5 (1997) 50-56
25. Unser M., and Aldroubi A. A review of wavelets in biomedical applications. Proceedings of the IEEE 84 4 (1996) 626-638
26. Akay M., Semmlow J.L., Welkowitz W., Bauer M.D., and Kostis J.B. Noninvasive detection of coronary stenoses before and after angioplasty using eigenvector methods. IEEE Transactions on Biomedical Engineering 37 11 (1990) 1095-1104
27. Kordylewski H., Graupe D., and Liu K. A novel large-memory neural network as an aid in medical diagnosis applications. IEEE Transactions on Information Technology in Biomedicine 5 3 (2001) 202-209
28. Übeyli E.D. Statistics over features for internal carotid arterial disorders detection. Computers in Biology and Medicine 38 3 (2008) 361-371
29. Andrzejak R.G., Lehnertz K., Mormann F., Rieke C., David P., and 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 (2001) 061907
30. EEG time series are available under 〈http://www.meb.uni-bonn.de/epileptologie/science/physik/eegdata.html〉.
31. Haykin S., and Li X.B. Detection of signals in chaos. Proceedings of the IEEE 83 1 (1995) 95-122
32. Abarbanel H.D.I., Brown R., and Kennel M.B. Lyapunov exponents in chaotic systems: their importance and their evaluation using observed data. International Journal of Modern Physics B 5 9 (1991) 1347-1375
33. Haykin S. Neural Networks: A Comprehensive Foundation (1994), Macmillan, New York
34. Hagan M.T., and Menhaj M.B. Training feedforward networks with the Marquardt algorithm. IEEE Transactions on Neural Networks 5 6 (1994) 989-993
35. Battiti R. First- and second-order methods for learning: between steepest descent and Newton's method. Neural Computation 4 (1992) 141-166