Characterization of entropy measures against data loss: Application to EEG records

This study is aimed at characterizing three signal entropy measures, Approximate Entropy (ApEn), Sample Entropy (SampEn) and Multiscale Entropy (MSE) over real EEG signals when a number of samples are randomly lost due to, for example, wireless data transmission. The experimental EEG database compri...

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Bibliographic Details
Main Authors: Roldan, E. M. C., Molina-Pico, A., Cuesta-Frau, D., Martinez, P. M., Crespo, S. O.
Format: Conference Proceeding
Language:English
Subjects:
Complexity theory
Electroencephalography
Entropy
Loss measurement
Physiology
Robustness
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Summary:This study is aimed at characterizing three signal entropy measures, Approximate Entropy (ApEn), Sample Entropy (SampEn) and Multiscale Entropy (MSE) over real EEG signals when a number of samples are randomly lost due to, for example, wireless data transmission. The experimental EEG database comprises two main signal groups: control EEGs and epileptic EEGs. Results show that both SampEn and ApEn enable a clear distinction between control and epileptic signals, but SampEn shows a more robust performance over a wide range of sample loss ratios. MSE exhibits a poor behavior for ratios over a 40% of sample loss. The EEG non-stationary and random trends are kept even when a great number of samples are discarded. This behavior is similar for all the records within the same group.
ISSN:1094-687X
1558-4615
DOI:10.1109/IEMBS.2011.6091509