Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1109/IEMBS.2011.6091509 | EMBC |
Keywords | Field | DocType |
entropy measures,medical information systems,eeg records,control eeg,epileptic eeg,electroencephalography,medical signal processing,sample entropy,sampen,approximate entropy,apen,data loss,entropy,multiscale entropy,physiology,robustness | Multiscale entropy,Approximate entropy,Sample entropy,Pattern recognition,Data loss,Wireless data transmission,Computer science,Robustness (computer science),Artificial intelligence,Statistics,Electroencephalography | Conference |
Volume | ISSN | ISBN |
2011 | 1557-170X | 978-1-4244-4122-8 |
Citations | PageRank | References |
1 | 0.48 | 3 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Eva M Cirugeda Roldán | 1 | 1 | 0.48 |
| Antonio Molina-Picó | 2 | 38 | 6.70 |
| D. Cuesta-Frau | 3 | 149 | 23.78 |
| Pau Miró-Martínez | 4 | 34 | 6.21 |
| Sandra Oltra-Crespo | 5 | 30 | 5.02 |