Wavelet entropy: a new tool for analysis of short duration brain electrical signals.

By Osvaldo Anibal Rosso, S. Blanco, J. Yordanova, V. Kolev, A. Figliola, M. Schürmann, E. Basar. Journal of Neuroscience Methods, 2001

843 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience

View Paper: https://www.semanticscholar.org/paper/db172c97f43722d52e8b1a9b160c7fb27e4044a6

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In Cognitive Neuroscience

Citations in the corpus

Rolling element bearing fault diagnosis using autocorrelation and continuous wavelet transform (2011, background): “The Shannon entropy appears as a measure of the degree of order/disorder of the signal, so it can provide useful information about the underlying dynamical process associated with the signal (Rosso et al., 2001).”
Event Detection in Twitter (2011, methodology): “Evaluating the Shannon Entropy (Shannon 1948) on distribution { ρ j } leads to the measurement of Shannon wavelet entropy (SWE) of signal S (Rosso et al. 2001): SWE measures the signal energy distribution at different scales (i.e. frequency bands).”
Feature extraction and classification for EEG signals using wavelet transform and machine learning techniques (2015, methodology): “The wavelet energy is a useful feature to classify the EEG signals corresponding to complex cognitive tasks, and it will be helpful for EEG classification in clinical applications, such as epilepsy, depression, and stress diagnosis as it is capable of identifying variations in non-stationary EEG sig”
Permutation Entropy and Its Main Biomedical and Econophysics Applications: A Review (2012, methodology): “Among others, we can mention frequency counting [6], procedures based on amplitude statistics [7], binary symbolic dynamics [8], Fourier analysis [9], or wavelet transform [10].”
Nonlinear multivariate analysis of neurophysiological signals. (2005, methodology): “Finally, a variant of Shannon entropy, in which probabilities are calculated not in the time domain but in the time-frequency representation of the EEG provided by wavelets (Quian Quiroga et al., 2001; Rosso et al., 2001), has been used to trace the response of rat’s brain to ischemia (Al-Nashash et”
Comparison of STFT and wavelet transform methods in determining epileptic seizure activity in EEG signals for real-time application (2005, methodology): “As a result, as we expressed in diIerent styles before, because of it has pre-focus (focus) property on short time high-frequency events realized in on signals in medical signal process, particularly in researching epileptic seizure of EEG signals, WT is very useful in the expression of discontinuit”