Abstract
In the recent past there has been a torrent of papers concerned with data processing, see e.g., Casdagli and Eubank [1]. In large part this flood of interest has been associated with the realization that irregular time series, long thought to contain signal and noise, may in fact be chaotic. Noise is produced by the coupling of a signal to an infinite dimensional environment, e.g., the billions of neurons in the brain producing the erratic EEG signal. Chaos on the other hand is produced by the intrinsic deterministic dynamics of a low-dimensional nonlinear system. Difficulties arise when one attempts to discriminate between colored noise and chaos in erratic time series. The difficulties are associated with how one interprets the erratic nature of the time series. If it is noise, the random character of the data masks the underlying signal. If it is chaos, the statistical fluctuations contain information and can not be conveniently suppressed as one would noise. However, since quantities such as the correlation or information dimension of the time series can not distinguish between the two, the development of new measures are important and new methods of data processing required.
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West, B.J., Zhang, W., Mackey, H.J. (1994). Chaos, Noise and Biological Data. In: Nonnenmacher, T.F., Losa, G.A., Weibel, E.R. (eds) Fractals in Biology and Medicine. Mathematics and Biosciences in Interaction. Birkhäuser, Basel. https://doi.org/10.1007/978-3-0348-8501-0_4
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DOI: https://doi.org/10.1007/978-3-0348-8501-0_4
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