LIST OF REFERENCES


[1] Box G.E.P. and Jenkins G.M., Time Series Analysis: Forecasting and Control,
 revised edition, Holden-Day, San Francisco, 1976.

[2] Basseville M. and Benveniste A., Detection of Abrupt Changes in Signals and
 Dynamical Systems, Lecture Notes in Control and Information Sciences, vol. 77,
 Springer-Verlag, Berlin, 1986

[3] Sandberg I.W., Lo T. J., Fancourt C.L., Principe J.C., Katagiri S., Haykin S.,
 Nonlinear Dynamical Systems: Feedforward Neural Network Perspectives
 (Adaptive and Learning Systems for Signal Processing, Communications and
 control), Wiley-Interscience, New York, 2001.

[4] Haan-Go Choi, Principe J.C., Hutchison A.A, Wozniak, J., A. Multiresolution
 segmentation of respiratory electromyographic signals, IEEE Trans. Biomedical
 Engineering, vol.41, pp 257-266, 1994

[5] Bernard M., Bouchoucha M., Cugnenc P, Analysis of medical signals by an
 automatic method of segmentation and classification without any a-priori
 knowledge, Proc. of Third Annual IEEE Symposium on Computer-Based Medical
 Systems, pp 381-388, 1990.

[6] Bhattacharya J., Detection of weak chaos in infant respiration, IEEE Trans. On
 Systems, Man and Cybernetics, Part B, vol. 31, no 4, pp 634-642, 2001

[7] Cohen M.E., Hudson D.L., Anderson M.F., Deedwania P.C., Using chaotic
 measures to summarize medical signal analysis, Proceedings of the First Joint
 Annual Fall Meeting of the Biomedical Engineering Society, vol. 2, p 904, 1999

[8] Wang, Y., Adali T., Kung S.-Y, Szabo Z., Quantification and segmentation of
 brain tissues from MR images: A probabilistic neural network approach, IEEE
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[9] Appel U. and Brandt A.V., Adaptive sequential segmentation of piecewise
 stationary time series, Inf. Sci., vol. 29, no.1, pp 27-56, 1982.