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Open AccessArticle

Measuring Instantaneous and Spectral Information Entropies by Shannon Entropy of Choi-Williams Distribution in the Context of Electroencephalography

1
Department ESAII, Centre for Biomedical Engineering Research, Universitat Politècnica de Catalunya, CIBER of Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), Barcelona 08028, Spain
2
Department IIE, Lleida University, LLeida, 25003, Spain
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Author to whom correspondence should be addressed.
Entropy 2014, 16(5), 2530-2548; https://doi.org/10.3390/e16052530
Received: 6 December 2013 / Revised: 30 April 2014 / Accepted: 5 May 2014 / Published: 9 May 2014
(This article belongs to the Special Issue Advances in Information Theory)
The theory of Shannon entropy was applied to the Choi-Williams time-frequency distribution (CWD) of time series in order to extract entropy information in both time and frequency domains. In this way, four novel indexes were defined: (1) partial instantaneous entropy, calculated as the entropy of the CWD with respect to time by using the probability mass function at each time instant taken independently; (2) partial spectral information entropy, calculated as the entropy of the CWD with respect to frequency by using the probability mass function of each frequency value taken independently; (3) complete instantaneous entropy, calculated as the entropy of the CWD with respect to time by using the probability mass function of the entire CWD; (4) complete spectral information entropy, calculated as the entropy of the CWD with respect to frequency by using the probability mass function of the entire CWD. These indexes were tested on synthetic time series with different behavior (periodic, chaotic and random) and on a dataset of electroencephalographic (EEG) signals recorded in different states (eyes-open, eyes-closed, ictal and non-ictal activity). The results have shown that the values of these indexes tend to decrease, with different proportion, when the behavior of the synthetic signals evolved from chaos or randomness to periodicity. Statistical differences (p-value < 0.0005) were found between values of these measures comparing eyes-open and eyes-closed states and between ictal and non-ictal states in the traditional EEG frequency bands. Finally, this paper has demonstrated that the proposed measures can be useful tools to quantify the different periodic, chaotic and random components in EEG signals. View Full-Text
Keywords: entropy; time-frequency representation; electroencephalography; complexity entropy; time-frequency representation; electroencephalography; complexity
MDPI and ACS Style

Melia, U.; Claria, F.; Vallverdu, M.; Caminal, P. Measuring Instantaneous and Spectral Information Entropies by Shannon Entropy of Choi-Williams Distribution in the Context of Electroencephalography. Entropy 2014, 16, 2530-2548.

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