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

Author

Roldán, Eva M Cirugeda ; Picó, Antonio Molina ; Frau, David Cuesta ; Martínez, Pau Miró ; Crespo, Sandra Oltra

Author_Institution

Comput. Sci. Dept., Politechnic Univ. of Valencia, Valencia, Spain

fYear

2011

fDate

Aug. 30 2011-Sept. 3 2011

Firstpage

6110

Lastpage

6113

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.

Keywords

electroencephalography; entropy; medical information systems; medical signal processing; ApEn; EEG records; SampEn; approximate entropy; control EEG; data loss; entropy measures; epileptic EEG; multiscale entropy; sample entropy; Complexity theory; Electroencephalography; Entropy; Loss measurement; Physiology; Robustness; Algorithms; Artifacts; Electroencephalography; Entropy; Humans; Information Storage and Retrieval; Reproducibility of Results; Seizures; Sensitivity and Specificity;

fLanguage

English

Publisher

ieee

Conference_Titel

Engineering in Medicine and Biology Society, EMBC, 2011 Annual International Conference of the IEEE

Conference_Location

Boston, MA

ISSN

1557-170X

Print_ISBN

978-1-4244-4121-1

Electronic_ISBN

1557-170X

Type

conf

DOI

10.1109/IEMBS.2011.6091509

Filename

6091509