6533b7d0fe1ef96bd125b8cc

RESEARCH PRODUCT

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

Antonio Molina-picóDavid Cuesta-frauSandra Oltra CrespoPau Miró I MartínezEva M. Cirugeda Roldan

subject

Computer scienceEntropyInformation Storage and RetrievalData lossElectroencephalographySensitivity and SpecificityApproximate entropyMultiscale entropyEntropy (classical thermodynamics)SeizuresStatisticsmedicineHumansEntropy (information theory)Entropy (energy dispersal)Entropy (arrow of time)medicine.diagnostic_testbusiness.industryEntropy (statistical thermodynamics)Reproducibility of ResultsElectroencephalographyPattern recognitionSample entropyArtificial intelligenceArtifactsbusinessAlgorithmsEntropy (order and disorder)

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.

yearjournalcountryeditionlanguage
2012-01-192011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://doi.org/10.1109/iembs.2011.6091509