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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| creator | Roldan, E. M. C. Molina-Pico, A. Cuesta-Frau, D. Martinez, P. M. Crespo, S. O. |
| description | 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. |
| doi_str_mv | 10.1109/IEMBS.2011.6091509 |
| format | Conference Proceeding |
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| identifier | ISSN: 1094-687X |
| ispartof | 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2011, Vol.2011, p.6110-6113 |
| issn | 1094-687X 1557-170X 1558-4615 |
| language | eng |
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| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Algorithms Complexity theory Electroencephalography Electroencephalography - methods Entropy Humans Information Storage and Retrieval - methods Loss measurement Physiology Reproducibility of Results Robustness Seizures - diagnosis Sensitivity and Specificity |
| title | Characterization of entropy measures against data loss: Application to EEG records |
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