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Article

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

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Technological Institute of Informatics, Universitat Politècnica de València, Alcoi Campus, 03801 Alcoi, Spain
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Department of Cybernetics, Czech Technical University in Prague, 16000 Prague, Czech Republic
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Internal Medicine Department, Teaching Hospital of Móstoles, 28935 Madrid, Spain
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Department of Diabetes, Diabetes Centre, Institute for Clinical and Experimental Medicine, 14021 Prague, Czech Republic
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Department of Medical Biochemistry and Laboratory Diagnostics, General University Hospital, Charles University in Prague 1st Faculty of Medicine, 12108 Prague, Czech Republic
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Hepatogastroenterology Department, Transplant centre, Institute for Clinical and Experimental Medicine, 14021 Prague, Czech Republic
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Obesitology Department, Institute of Endocrinology, 11694 Prague, Czech Republic
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Experimental Medicine Centre, Institute for Clinical and Experimental Medicine, 14021 Prague, Czech Republic
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Author to whom correspondence should be addressed.
Entropy 2018, 20(11), 871; https://doi.org/10.3390/e20110871
Received: 8 October 2018 / Revised: 7 November 2018 / Accepted: 9 November 2018 / Published: 12 November 2018
(This article belongs to the Special Issue The 20th Anniversary of Entropy - Approximate and Sample Entropy)
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. View Full-Text
Keywords: sample entropy; fuzzy entropy; blood glucose; signal classification sample entropy; fuzzy entropy; blood glucose; signal classification
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MDPI and ACS Style

Cuesta-Frau, D.; Novák, D.; Burda, V.; Molina-Picó, A.; Vargas, B.; Mraz, M.; Kavalkova, P.; Benes, M.; Haluzik, M. Characterization of Artifact Influence on the Classification of Glucose Time Series Using Sample Entropy Statistics. Entropy 2018, 20, 871. https://doi.org/10.3390/e20110871

AMA Style

Cuesta-Frau D, Novák D, Burda V, Molina-Picó A, Vargas B, Mraz M, Kavalkova P, Benes M, Haluzik M. Characterization of Artifact Influence on the Classification of Glucose Time Series Using Sample Entropy Statistics. Entropy. 2018; 20(11):871. https://doi.org/10.3390/e20110871

Chicago/Turabian Style

Cuesta-Frau, David, Daniel Novák, Vacláv Burda, Antonio Molina-Picó, Borja Vargas, Milos Mraz, Petra Kavalkova, Marek Benes, and Martin Haluzik. 2018. "Characterization of Artifact Influence on the Classification of Glucose Time Series Using Sample Entropy Statistics" Entropy 20, no. 11: 871. https://doi.org/10.3390/e20110871

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