Analysis of early hypoxia EEG based on a novel chaotic neural network

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 4232 LNCS, Issue , 2006, Pages 11-18

  Hu, Meng ^a   Li, Jiaojie ^b   Li, Guang ^c   Freeman, Walter J ^d  

^a ZHEJIANG UNIVERSITY   (China)

^b Hangzhou Sanitarium of PLA Airforce ^*  (China)

^c STATE KEY LABORATORY OF INDUSTRIAL CONTROL TECHNOLOGY   (China)

^d UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

Chaotic neural network; EEG; Hypoxia

Indexed keywords

CHAOS THEORY; DATA PROCESSING; ELECTROENCEPHALOGRAPHY; NEUROLOGY; PATTERN RECOGNITION;

CHAOTIC NEURAL NETWORK; HYPOXIA; KILL MODEL;

NEURAL NETWORKS;

EID: 33750593239     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/11893028_2     Document Type: Conference Paper

References (11)

1. Saroj, K. L. Lal., Ashley, Craig.: A critical review of the psychophysiology of driver fatigue. Biological Psychology. 55 (2001) 173-194
2. Vuckovic, A., Andrew, R. V., Chen, C. N., Popovic, D.: Automatic recognition of alertness and drowsiness from EEG by an artificial neural network. Medical Engineering & Physics. 24 (2002) 349-360
3. Zhang, X. S., Rob, J.: EEG complexity as a measure of depth of anesthesia for patients. IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING. 48 (2001) 12
4. Kozma, R., Freeman, W. J.: Chaotic Resonance - Methods and Applications for Robust Classification of Noisy and Variable Patterns. Int. J. Bifurcation and Chaos. 11(6) (2001) 1607-1629
5. Li, G., Lou, Z., Wang, L., Li, X. and Freeman, W. J.: Application of Chaotic Neural Model Based on Olfactory System on Pattern Recognitions. In: Wang, L., Chen, K., Ong Y.S. (eds.): Advances in Natural Computation. Lecture Notes in Computer Science, Vol. 3610. Springer-Verlag, Berlin Heidelberg New York (2005) 378-381
6. Quarder, S., Claussnitzer, U., Otto, M.: Using Singular-Value Decompositions to Classify Spatial Patterns Generated by a Nonlinear Dynamic Model of the Olfactory System. Chemometrics and Intelligent Laboratory Systems. 59 (2001) 45-51
7. Hu, M., Li, J. J., Li, G., Tang, X. W. and Freeman, W. J.: Normal and Hypoxia EEG Recog-nition Based on a Chaotic Olfactory Model. In: Wang, J., et al. (eds.): ISNN 2006. Lecture Notes in Computer Science, Vol. 3610. Springer-Verlag, Berlin Heidelberg New York (2006) 554-559
8. Chang, H. J., Freeman, W. J.: Optimization of Olfactory Model in Software to Give 1/f Power Spectra Reveals Numerical Instabilities in Solutions Governed by Aperiodic (Chaotic) Attractors. Neural Networks. 11 (1998) 449-466
9. Chang, H. J., Freeman, W. J., Burke, B. C.: Biologically Modeled Noise Stabilizing Neuro-dynamics for Pattern Recognition. Int, J. Bifurcation and Chaos. 8(2) (1998) 321-345
10. Freeman, W. J., Chang, H. J., Burke, B. C., Rose, P. A., Badler, J.: Taming Chaos: Stabilization of Aperiodic Attractors by Noise. IEEE Transactions on Circuits and Systems. 44 (1997) 989-996
11. Principe, J. C., Tavares, V. G., Harris, J. G., Freeman, W. J.: Design and Implementation of a Biologically Realistic Olfactory Cortex in Analog VLSI. Proceedings of the IEEE. 89(7) (2001) 1030-1051