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Entropy 2014, 16(11), 5901-5918; doi:10.3390/e16115901

Comparative Study of Entropy Sensitivity to Missing Biosignal Data

1
Technological Institute of Informatics, Polytechnic University of Valencia, Alcoi Campus, Plaza Ferrandiz y Carbonell, 2, Alcoi 03801, Spain
2
Department of Statistics, Polytechnic University of Valencia, Alcoi Campus, Plaza Ferrandiz y Carbonell, 2, Alcoi 03801, Spain
*
Author to whom correspondence should be addressed.
Received: 3 July 2014 / Revised: 5 August 2014 / Accepted: 3 November 2014 / Published: 10 November 2014
(This article belongs to the Special Issue Entropy and Electroencephalography)
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Abstract

Entropy estimation metrics have become a widely used method to identify subtle changes or hidden features in biomedical records. These methods have been more effective than conventional linear techniques in a number of signal classification applications, specially the healthy–pathological segmentation dichotomy. Nevertheless, a thorough characterization of these measures, namely, how to match metric and signal features, is still lacking. This paper studies a specific characterization problem: the influence of missing samples in biomedical records. The assessment is conducted using four of the most popular entropy metrics: Approximate Entropy, Sample Entropy, Fuzzy Entropy, and Detrended Fluctuation Analysis. The rationale of this study is that missing samples are a signal disturbance that can arise in many cases: signal compression, non-uniform sampling, or data transmission stages. It is of great interest to determine if these real situations can impair the capability of segmenting signal classes using such metrics. The experiments employed several biosignals: electroencephalograms, gait records, and RR time series. Samples of these signals were systematically removed, and the entropy computed for each case. The results showed that these metrics are robust against missing samples: With a data loss percentage of 50% or even higher, the methods were still able to distinguish among signal classes. View Full-Text
Keywords: approximate entropy; sample entropy; fuzzy entropy; detrended fluctuation analysis; biosignal classification; data loss approximate entropy; sample entropy; fuzzy entropy; detrended fluctuation analysis; biosignal classification; data loss
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Cirugeda-Roldan, E.; Cuesta-Frau, D.; Miro-Martinez, P.; Oltra-Crespo, S. Comparative Study of Entropy Sensitivity to Missing Biosignal Data. Entropy 2014, 16, 5901-5918.

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