Abstract
An analysis of physiological wake/sleep data is presented. We apply a recent method for the analysis of nonstationary time series with multiple operating modes. In particular, it is possible to detect and to model a switching of the dynamics and also a less abrupt, time consuming drift from one mode to another. This is achieved by an unsupervised algorithm that segments the data according to inherent modes, and a subsequent search through the space of possible drifts. The application to wake/sleep data demonstrates that analysis and modeling of real-world time series can be improved when the drift paradigm is taken into account. In the case of wake/sleep data, we hope to gain more insight into the physiological processes that are involved in the transition from wake to sleep.
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© 1997 Springer-Verlag Berlin Heidelberg
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Kohlmorgen, J., Müller, K.R., Rittweger, J., Pawelzik, K. (1997). Analysis of wake/sleep EEG with competing experts. In: Gerstner, W., Germond, A., Hasler, M., Nicoud, JD. (eds) Artificial Neural Networks — ICANN'97. ICANN 1997. Lecture Notes in Computer Science, vol 1327. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0020296
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DOI: https://doi.org/10.1007/BFb0020296
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