Characterization of Artifact Influence on the Classification of Glucose Time Series Using Sample Entropy Statistics

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11110%2F18%3A10385530" target="_blank" >RIV/00216208:11110/18:10385530 - isvavai.cz</a>

Alternative codes found

RIV/68407700:21230/18:00326968 RIV/00023761:_____/18:N0000005 RIV/00023001:_____/18:00077455 RIV/00064165:_____/18:10385530

Result on the web

<a href="https://doi.org/10.3390/e20110871" target="_blank" >https://doi.org/10.3390/e20110871</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3390/e20110871" target="_blank" >10.3390/e20110871</a>

Result language

angličtina

Original language name

Characterization of Artifact Influence on the Classification of Glucose Time Series Using Sample Entropy Statistics

Original language description

This paper analyses the performance of SampEn and one of its derivatives, Fuzzy Entropy (FuzzyEn), in the context of artifacted blood glucose time series classification. This is a difficult and practically unexplored framework, where the availability of more sensitive and reliable measures could be of great clinical impact. Although the advent of new blood glucose monitoring technologies may reduce the incidence of the problems stated above, incorrect device or sensor manipulation, patient adherence, sensor detachment, time constraints, adoption barriers or affordability can still result in relatively short and artifacted records, as the ones analyzed in this paper or in other similar works. This study is aimed at characterizing the changes induced by such artifacts, enabling the arrangement of countermeasures in advance when possible. Despite the presence of these disturbances, results demonstrate that SampEn and FuzzyEn are sufficiently robust to achieve a significant classification performance, using records obtained from patients with duodenal-jejunal exclusion. The classification results, in terms of area under the ROC of up to 0.9, with several tests yielding AUC values also greater than 0.8, and in terms of a leave-one-out average classification accuracy of 80%, confirm the potential of these measures in this context despite the presence of artifacts, with SampEn having slightly better performance than FuzzyEn.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

10608 - Biochemistry and molecular biology

Project

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Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2018

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Entropy

ISSN

1099-4300

e-ISSN

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Volume of the periodical

20

Issue of the periodical within the volume

11

Country of publishing house

CH - SWITZERLAND

Number of pages

18

Pages from-to

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UT code for WoS article

000451308800063

EID of the result in the Scopus database

2-s2.0-85057037354