Single-Channel EEG Analysis - Quantitative EEG Analysis Methods and Clinical Applications - page 106

[79] Iasemidis, L. D., and J. C. Sackellares, “The Temporal Evolution of the Largest Lyapunov
Exponent on the Human Epileptic Cortex,” in Measuring Chaos in the Human Brain, D. W.
Duke and W. S. Pritchard, (eds.), Singapore: World Scientific, 1991, pp. 49-82.
[80] Iasemidis, L. D., J. C. Principe, and J. C. Sackellares, “Measurement and Quantification of
Spatiotemporal Dynamics of Human Epileptogenic Seizures,” in Nonlinear Biomedical Sig-
nal Processing, M. Akay, (ed.), Piscataway, NJ: IEEE Press, 2000, Vol. II, pp. 294-318.
[81] Iasemidis, L. D., et al., “Phase Space Topography of the Electrocorticogram and the
Lyapunov Exponent in Partial Seizures,” Brain Topogr . , Vol. 2, 1990, pp. 187-201.
[82] Iasemidis, L. D., et al., “Spatio-Temporal Evolution of Dynamic Measures Precedes Onset
of Mesial Temporal Lobe Seizures,” Epilepsia, Vol. 35S, 1994, p. 133.
[83] Iasemidis, L. D., et al., “Quadratic Binary Programming and Dynamic System Approach to
Determine the Predictability of Epileptic Seizures,” J . Combinatorial Optimization, Vol. 5,
2001, pp. 9-26.
[84] Iasemidis, L. D., et al., “Long-Term Prospective On-Line Real-Time Seizure Prediction,” J .
Clin . Neurophysiol . , Vol. 116, 2005, pp. 532-544.
[85] Iasemidis, L. D., et al., “Adaptive Epileptic Seizure Prediction System,” IEEE Trans . on
Biomed . Eng . , Vol. 50, No. 5, 2003, pp. 616-627.
[86] Iasemidis, L. D., et al., “On the Prediction of Seizures, Hysteresis and Resetting of the Epi-
leptic Brain: Insights from Models of Coupled Chaotic Oscillators,” in Order and Chaos, T.
Bountis and S. Pneumatikos, (eds.), Thessaloniki, Greece: Publishing House K. Sfakianakis,
2003, Vol. 8, pp. 283-305.
[87] Prasad, A., et al., “Dynamic Hysteresis and Spatial Synchronization in Coupled Nonidenti-
cal Chaotic Oscillators,” Pramana J . Phys . , Vol. 64, 2005, pp. 513-523.
[88] Tsakalis, K., and L. D. Iasemidis, “Control Aspects of a Theoretical Model for Epileptic Sei-
zures,” Int . J . Bifurcations Chaos, Vol. 16, 2006, pp. 2013-2027.
[89] Chakravarthy, N., et al., “Modeling and Controlling Synchronization in a Neuron-Level
Population Model,” Int . J . Neural Systems, Vol. 17, 2007, pp. 123-138.
[90] Iasemidis, L. D., “Epileptic Seizure Prediction and Control,” IEEE Trans . on Biomed . Eng . ,
Vol. 50, No. 5, 2003, pp. 549-558.
[91] Shannon, C., “A Mathematical Theory of Communication,” Bell . Syst . Tech . J . , Vol. 27,
No. 3, 1948, pp. 379-423.
[92] Pincus, S., “Approximate Entropy as a Measure of System Complexity,” Proc . Natl . Acad .
Sci . USA, Vol. 88, No. 6, 1991, pp. 2297-2301.
[93] Pincus, S., “Approximate Entropy (ApEn) as a Complexity Measure,” Chaos, Vol. 5, 1995,
p. 110.
[94] Aftanas, L., et al., “Non-Linear Analysis of Emotion EEG: Calculation of Kolmogorov
Entropy and the Principal Lyapunov Exponent,” Neurosci . Lett . , Vol. 226, No. 1, 1997, pp.
13-16.
[95] Rosso, O., et al., “Wavelet Entropy: A New Tool for Analysis of Short Duration Brain Elec-
trical Signals,” J . Neurosci . Methods, Vol. 105, No. 1, 2001, pp. 65-75.
[96] Inouye, T., et al., “Quantification of EEG Irregularity by Use of the Entropy of the Power
Spectrum,” Electroencephalogr . Clin . Neurophysiol . , Vol. 79, No. 3, 1991, pp. 204-210.
[97] Bezerianos, A., S. Tong, and N. Thakor, “Time-Dependent Entropy Estimation of EEG
Rhythm Changes Following Brain Ischemia,” Ann . Biomed . Eng . , Vol. 31, No. 2, 2003, pp.
221-232.
[98] Tong, S., et al., “Parameterized Entropy Analysis of EEG Following Hypoxic-Ischemic
Brain Injury,” Phys . Lett . A, Vol. 314, No. 5-6, 2003, pp. 354-361.
[99] Tong, S., et al., “Nonextensive Entropy Measure of EEG Following Brain Injury from Car-
diac Arrest,” Physica A, Vol. 305, No. 3-4, 2002, pp. 619-628.
[100] Li, X., “Wavelet Spectral Entropy for Indication of Epileptic Seizure in Extracranial EEG,”
Lecture Notes in Computer Science , Vol. 4234, 2006, p. 66.