Biomedical Engineering Reference
In-Depth Information
[33]
Rosso, O., et al., “Wavelet Entropy: A New Tool for Analysis of Short Duration Brain Elec-
trical Signals,” J. Neurosci. Methods, Vol. 105, 2001, pp. 65-75.
[34]
Ghosh-Dastidar, S., H. Adeli, and N. Dadmehr, “Mixed-Band Wavelet-Chaos-Neural Net-
work Methodology for Epilepsy and Epileptic Seizure Detection,” IEEE Trans. on Biomed.
Eng., Vol. 54, No. 9, September 2007, pp. 1545-1551.
[35]
Al-Nashash, H., et al., “Wavelet Entropy for Subband Segmentation of EEG During Injury
and Recovery,” Ann. Biomed. Eng ., Vol. 31, 2003, pp. 1-6.
[36]
Kassebaum, J., et al., “Observations from Chaotic Analysis of Sleep EEGs,” 28th Annual
International Conference of the IEEE Engineering in Medicine and Biology Society , 2006,
pp. 2126-2129.
[37]
Al-Nashash, H., and N. V. Thakor, “Monitoring of Global Cerebral Ischemia Using Wave-
let Entropy Rate of Change,” IEEE Trans. on Biomed. Eng., Vol. 52, No. 12, December
2005, pp. 2019-2022.
[38]
Alan, V., W. Ronald, and R. John, Discrete-Time Signal Processing, Upper Saddle River,
NJ: Prentice-Hall, 1999.
[39]
Rangaraj, M., Biomedical Signal Analysis: A Case-Study Approach, New York: Wiley-IEEE
Press, 2002.
[40]
Charles, S., Signal Processing of Random Physiological Signals, San Rafael, CA: Morgan &
Claypool, 2006.
[41]
John, L., Biosignal and Biomedical Image Processing, MATLAB-Based Applications, New
York: Marcel Dekker, 2004.
[42]
All, A., et al., “Using Spectral Coherence for the Detection and Monitoring of Spinal Cord
Injury,” Proc. 4th GCC Industrial Electrical Electronics Conf., Manama, Bahrain, 2007.
[43]
Fatoo, N., et al., “Detection and Assessment of Spinal Cord Injury Using Spectral Coher-
ence,” 29th International Conference of the IEEE Engineering in Medicine and Biology
Society in conjunction with the Biennial Conference of the French Society of Biological and
Medical Engineering (SFGBM), Lyon, France, 2007.
[44]
Nuwer, M., “Fundamentals of Evoked Potentials and Common Clinical Applications
Today,” Electroencephalog. Clin. Neurophysiol., Vol. 106, 1998, pp. 142-148.
[45]
Akaike, H., “A New Look at the Statistical Model Identification,” IEEE Trans. on Auto-
matic Control, Vol. AC-19, 1974, pp. 716-723.
[46]
Sanei, S., and J. A. Chambers, EEG Signal Processing, New York: Wiley, 2007.
[47]
Akay, M., (ed.), Time Frequency and Wavelets in Biomedical Signal Processing, New York:
Wiley-IEEE Press, 1997.
[48]
Grossmann, A., and J. Morlet, “Decomposition of Hardy Functions into Square Integrable
Wavelets of Constant Shape,” SIAM J. Math. Anal., Vol. 15, 1984, pp. 723-736.
[49]
Mallat, S., “A Theory for Multiresolution Signal Decomposition: The Wavelet Representa-
tion,” IEEE Trans. on Pattern Anal. Machine Intell., Vol. 11, 1989, pp. 674-693.
[50]
Daubechies, I., “The Wavelet Transform, Time-Frequency Localization, and Signal Analy-
sis,” IEEE Trans. on Info. Theory , Vol. 36, 1990, pp. 961-1050.
[51]
Strang, G., and T. Nguyen, Wavelets and Filter Banks, Wellesley, MA: Wellesley-Cam-
bridge Press, 1996.
[52]
Stéphane, M., A Wavelet Tour of Signal Processing, 2nd ed., New York: Academic Press,
1999.
[53]
Fraedrich, K., “Estimating the Dimensions of Weather and Climate Attractors,” J. Atmos.
Sci., Vol. 43, 1986, pp. 419-423.
[54]
Freeman, W. J., “Simulation of Chaotic EEG Patterns with a Dynamic Model of the Olfac-
tory System,” Biol. Cybern., Vol. 56, 1987, pp. 139-150.
[55]
Iasemidis, L. D., and J. C. Sackellares, “Chaos Theory in Epilepsy,” The Neuroscientist,
Vol. 2, 1996, pp. 118-126.
[56]
Dwyer, G. P., Jr., Nonlinear Time Series and Financial Applications , Tech. Rep., Federal
Reserve Bank of Atlanta library, 2003.
Search WWH ::




Custom Search