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Comput Biol Med. 2017 Aug 1;87:141-151. doi: 10.1016/j.compbiomed.2017.05.028. Epub 2017 May 31.

Noisy EEG signals classification based on entropy metrics. Performance assessment using first and second generation statistics.

Author information

1
Technological Institute of Informatics, Polytechnic University of Valencia, Alcoi Campus, Plaza Ferrandiz y Carbonell 2, Alcoi, Spain. Electronic address: dcuesta@disca.upv.es.
2
Department of Statistics, Polytechnic University of Valencia, Alcoi Campus, Alcoi, Spain.
3
Technological Institute of Informatics, Polytechnic University of Valencia, Alcoi Campus, Plaza Ferrandiz y Carbonell 2, Alcoi, Spain.

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.

KEYWORDS:

Approximate Entropy; EEG artifacts; Electroencephalograms; Fuzzy Entropy; Sample Entropy; Signal classification

[Indexed for MEDLINE]

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