Symbolic representation of the EEG for sleep stage classification

International Conference on Intelligent Systems Design and Applications, ISDA

Volumn , Issue , 2011, Pages 253-258

  Herrera, L J ^a   Mora, A M ^a   Fernandes, C ^a,b   Migotina, D ^b   Guillen A ^a   Rosa, A C ^b  

^a UNIVERSITY OF GRANADA   (Spain)

^b UNIVERSITY OF LISBON   (Portugal)

Author keywords

EEG signal processing; Sleep Stage Classification; Support Vector Machines; Unbalanced Data; Variable Selection

Indexed keywords

EEG SIGNAL PROCESSING; SLEEP STAGE; SUPPORT VECTOR; UNBALANCED DATA; VARIABLE SELECTION;

BIOELECTRIC PHENOMENA; CONFORMAL MAPPING; INTELLIGENT SYSTEMS; SIGNAL PROCESSING; SUPPORT VECTOR MACHINES; SYSTEMS ANALYSIS; VISUALIZATION;

SLEEP RESEARCH;

EID: 84857519282     PISSN: 21647143     EISSN: 21647151     Source Type: Conference Proceeding    
DOI: 10.1109/ISDA.2011.6121664     Document Type: Conference Paper

References (20)

1. P. Anderer et al. "An E-Health Solution for Automatic Sleep Classification according to Rechtschaffen and Kales: Validation Study of the Somnolyzer 24 x 7 Utilizing the Siesta Database,"Neurophycobiology, vol 51, no. 3, 2005.
2. R. Norman, I. Pal, C. Stewart, J. Walsleben, D. Rappaport, "Interobserver Agreement Among Sleep Scorers From Different Centers in a Large Dataset," Sleep, vol.23, pp. 901-908, 2000.
3. A. Rechtschaffen and A. Kales, "A manual of standardized terminology, techniques and scoring system for sleep stages of human subjects," Ser. National Institutes of Health (U.S.). Bethesda, Md: U. S. National Institute of Neurological Diseases and Blindness, Neurological Information Network, 1968, vol. 204.
4. C. Holzmarn, C. Perez, C. Held, M. S. Mann, F.Pizano, J. Prez, M. Garrido, and P. Peirano, "Expert system classification of sleep-waking states in infants," Medical & Biological Engineering & Computing, vol. 37, pp. 466-476, 1999. (Pubitemid 29391135)
5. J. E. Heiss, C. M. Held, P. A. Estvez, C. A. Perez C. A. Holzmann, and J. P. Prez, Classification of sleep stages in infants: a neuro fuzzy approach," Engineering in Medicine and Biology Magazine, vol. 21, no. 5,pp. 147-151,2002.
6. S. Gudmundsson, T. Runarsson, and S. Sigurdsson, Automatic sleep staging using support vector machines with posterior probability estimates," in Pro. of the International Conference on Computational Intelligence for Modelling, Control, 2005, pp. 366-373.
7. E. Estrada, H. Nazeran, P. Nava, K. Behbehani, J. Burk, E. Lucas, "EEG feature extraction for classification of sleep stages," Engineering in Medicine and Biology Society, 2004. IEMBS '04. 26th Annual International Conference of the IEEE, 2004, pp. 196-199
8. A.M. Mora, C.M. Fernandes, L.J. Herrera, P.A. Castillo, J.J. Merelo, F. Rojas, A. Rosa, Sleeping th Ants, SVMs, multilayer perceptrons and SOMs," 10th International Conference on Intelligent Systems Design and Applications, 2010, pp: 126-131
9. R. Largo, C. Munteanu, and A. Rosa, "Cap event detection by wavelets and GA tuning," in Proceedings of the IEEE International Workshop on Intelligent Signal Processing, 2005, pp.44-48.
10. A. Mora, C. Fernandes, J. Merelo, V. Ramos, J. Laredo, and A. Rosa, "Kohonants: a self-organizing ant algorithm for clustering and pattern classification," in Artificial Life XI, Procs. 11th Conference on the Simulation and Synthesis of Living Systems, S. B. et al. Ed. MIT Press, August 2008, pp.428-435.
11. Handbook of Electroencephalography and Slinical Neurophysiology, Vol. I: Methods of Analysis of Brain Electrical and Magnetic Signals, edited by A. Gevins and A. Rémond (Elseiver, Amsterdam, 1987).
12. D. Migotina, A. Rosa, Segmentation of Sleep EEG Signal by Optimal Thresholds", submitted to BioMed 2012, Innsbruck, Austria
13. PA. Estevez M. Tesmer, C.A. Perez, J.M. Zurada, Normalized Mutual Information Feature Selection," IEEE Transactions on Neural Networks, 20(2), pp.: 189-201,2009.
14. 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 (Pubitemid 41245053)
15. C.-W. Hsu and C-J. Lin, "A comparison of methods for multiclass support vector machines," IEEE Transactions on Neural Networks, vol. 13, no.2, pp.4I5-425,2002.
16. E. Osuna, R. Freund, F. Girosi, Support Vector Machines: Training and applications. Al Memo 1 602, Massachuseffs Institute of Technology, 1997.
17. C. Chang and C. Lin, LIBSVM: a Library for Support Vector Machines, 2001, software available at http://www.csie.ntu.edu.tw/ cjlin/libsvm.
18. B. Schölkopf and A. Smola, Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond, Cambridge, USA: MIT Press, 2001.
19. T. Kohonen, The Self-Organizing Maps. Springer, 2001.
20. S. Ultsch, "Kohonen's self-organizing maps for exploratory data analysis," in Proc. of the INNC'90, 2000, pp. 305-308.