Comparative Study of Entropy Sensitivity to Missing Biosignal Data

Entropy

Eva Cirugeda-Roldan¹  David Cuesta-Frau¹  Pau Miro-Martinez² 

[1] Technological Institute of Informatics, Polytechnic University of Valencia, Alcoi Campus, Plaza Ferrandiz y Carbonell, 2, Alcoi 03801, Spain; E-Mails:;Department of Statistics, Polytechnic University of Valencia, Alcoi Campus, Plaza Ferrandiz y Carbonell, 2, Alcoi 03801, Spain; E-Mail:

关键词: approximate entropy;    sample entropy;    fuzzy entropy;    detrended fluctuation analysis;    biosignal classification;    data loss;   

DOI  :  10.3390/e16115901

来源: mdpi

【 摘 要 】

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.

【 授权许可】

CC BY   
© 2014 by the authors; licensee MDPI, Basel, Switzerland