Abstract
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.
Highlights
The main idea of diabetes control is assessing a time series [1]
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 [12,13,14,15,16] can still result in relatively short and artifacted records, as the ones analyzed in this paper
Sample Entropy (SampEn) was first introduced in [8], as an improvement of Approximate Entropy (ApEn), and Fuzzy Entropy (FuzzyEn) in [9], as an enhancement of SampEn. These methods were devised to characterize the level of irregularity, complexity, randomness or predictability found in time series, which is related to the dynamics of many physiological systems [52], as is the case for the gluco-regulatory system
Summary
The main idea of diabetes control is assessing a time series (blood glucose) [1] This was performed by means of punctual fasting blood measurements. A slight improvement was self-monitoring through serial capillary blood controls performed by the patient [2] Again, this was cumbersome and offered a poor overview of the time series. An important step was the use of glycosylated Hemoglobin (HbA1c), which provided an integrated assessment of the glucose blood levels of the last 8–12 weeks [3]. This became a standard of care for many years.
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