Classification of glucose records from patients at diabetes risk using a combined permutation entropy algorithm

•First time permutation entropy is applied to glucose time series.•Test of different customizations for permutation entropy in order to address equal values and amplitude variations.•Prediction of evolution to diabetes based on a permutation entropy analysis of the glucose time series. Background an...

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Bibliographic Details
Published in:Computer methods and programs in biomedicine 2018-10, Vol.165, p.197-204
Main Authors: Cuesta–Frau, D., Miró–Martínez, P., Oltra–Crespo, S., Jordán–Núñez, J., Vargas, B., Vigil, L.
Format: Article
Language:English
Subjects:
Algorithms
Area Under Curve
Blood Glucose - metabolism
Blood Glucose Self-Monitoring - statistics & numerical data
Continuous glucose monitoring
Diabetes
Diabetes Mellitus - blood
Diabetes Mellitus - diagnosis
Diagnosis, Computer-Assisted - methods
Diagnosis, Computer-Assisted - statistics & numerical data
Early Diagnosis
Electronic Health Records - classification
Electronic Health Records - statistics & numerical data
Humans
Permutation entropy
Predictive Value of Tests
Risk Factors
Signal classification
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Summary:•First time permutation entropy is applied to glucose time series.•Test of different customizations for permutation entropy in order to address equal values and amplitude variations.•Prediction of evolution to diabetes based on a permutation entropy analysis of the glucose time series. Background and objectives: The adoption in clinical practice of electronic portable blood or interstitial glucose monitors has enabled the collection, storage, and sharing of massive amounts of glucose level readings. This availability of data opened the door to the application of a multitude of mathematical methods to extract clinical information not discernible with conventional visual inspection. The objective of this study is to assess the capability of Permutation Entropy (PE) to find differences between glucose records of healthy and potentially diabetic subjects. Methods: PE is a mathematical method based on the relative frequency analysis of ordinal patterns in time series that has gained a lot of attention in the last years due to its simplicity, robustness, and performance. We study in this paper the applicability of this method to glucose records of subjects at risk of diabetes in order to assess the predictability value of this metric in this context. Results: PE, along with some of its derivatives, was able to find significant differences between diabetic and non–diabetic patients from records acquired up to 3 years before the diagnosis. The quantitative results for PE were 3.5878 ± 0.3916 for the nondiabetic class, and 3.1564 ± 0.4166 for the diabetic class. With a classification accuracy higher than 70%, and by means of a Cox regression model, PE demonstrated that it is a very promising candidate as a risk stratification tool for continuous glucose monitoring. Conclusion: PE can be considered as a prospective tool for the early diagnosis of the glucoregulatory system.
ISSN:0169-2607
1872-7565
DOI:10.1016/j.cmpb.2018.08.018