Paper Info
Title
Noisy EEG signals classification based on entropy metrics. Performance assessment using first and second generation statistics.
Abstract
This paper evaluates the performance of first generation entropy metrics, featured by the well known and widely used Approximate Entropy (ApEn) and Sample Entropy (SampEn) metrics, and what can be considered an evolution from these, Fuzzy Entropy (FuzzyEn), in the Electroencephalogram (EEG) signal classification context. The study uses the commonest artifacts found in real EEGs, such as white noise, and muscular, cardiac, and ocular artifacts. Using two different sets of publicly available EEG records, and a realistic range of amplitudes for interfering artifacts, this work optimises and assesses the robustness of these metrics against artifacts in class segmentation terms probability. The results show that the qualitative behaviour of the two datasets is similar, with SampEn and FuzzyEn performing the best, and the noise and muscular artifacts are the most confounding factors. On the contrary, there is a wide variability as regards initialization parameters. The poor performance achieved by ApEn suggests that this metric should not be used in these contexts.
Year
DOI
Venue
2017
10.1016/j.compbiomed.2017.05.028
Computers in Biology and Medicine
Keywords
Field
DocType
Electroencephalograms,Signal classification,Approximate Entropy,Sample Entropy,Fuzzy Entropy,EEG artifacts
Approximate entropy,Sample entropy,Pattern recognition,Computer science,Segmentation,Fuzzy logic,Robustness (computer science),White noise,Artificial intelligence,Initialization,Statistics,Electroencephalography
Journal
Volume
ISSN
Citations 
87
0010-4825
2
PageRank 
References 
Authors
0.42
18
5
Name
Order
Citations
PageRank
D. Cuesta-Frau114923.78
Pau Miró-Martínez2346.21
Jorge Jordán Núñez330.79
Sandra Oltra-Crespo4305.02
Antonio Molina-Picó5386.70