title: Characterization of entropy measures against data loss: application to EEG records.
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.
authors.affiliation: Computer Science Department, Politechnic University of Valencia, EPSA, Alcoy Campus, 03801 Alcoy, Alicante, Spain. ecirugeda@giica.com
authors.forename: Eva M Cirugeda
authors.initials: EM
authors.lastname: Roldán
authors.forename: Antonio
authors.initials: A
authors.lastname: Molina-Picó
authors.forename: David
authors.initials: D
authors.lastname: Cuesta-Frau
authors.forename: Pau Miró
authors.initials: PM
authors.lastname: Martínez
authors.forename: Sandra Oltra
authors.initials: SO
authors.lastname: Crespo
mesh_terms: D000465:Algorithms; D016477:Artifacts; D004569:Electroencephalography / Q000379:methods*; D019277:Entropy; D006801:Humans; D016247:Information Storage and Retrieval / Q000379:methods*; D015203:Reproducibility of Results; D012640:Seizures / Q000175:diagnosis*; D012680:Sensitivity and Specificity