A Theory for Multiresolution Signal Decomposition: The Wavelet Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
An introduction to wavelets
Multirate systems and filter banks
Multirate systems and filter banks
Joint time-frequency analysis: methods and applications
Joint time-frequency analysis: methods and applications
Wavelets, statistics, and biomedical application
SSAP '96 Proceedings of the 8th IEEE Signal Processing Workshop on Statistical Signal and Array Processing (SSAP '96)
A wavelet-based estimating depth of anesthesia
Engineering Applications of Artificial Intelligence
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In this paper we describe a new approach to quantify the concepts of order and complexity of EEG signals during sleep. Based on the concepts of entropy and wavelet transform, we introduce a measure named Wavelet-Entropy and we will show the results of its application to real sleep EEG signals and artificial data. The definition of wavelet entropy is very similar to the definition of entropy in information theory or spectral entropy in signal processing but its most important difference is the usage of time-frequency representation of the signal and its wavelet coefficients. Time-frequency methods proved to be very useful for signals like EEG with fast changing dynamics and high non-stationarity. This characteristic of EEG limits the usage of other complexity evaluation methods like chaos analysis and parameters like correlation dimension or Lyapunov exponent.