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

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.

Embedded Dimension and Time Series Length. Practical Influence on Permutation Entropy and Its Applications

“…Random values are also a reference dataset in many works, such as in [ 36 ], where sequences of 2000 uniform random numbers were used in some experiments. Spikes have been used in studies such as [ 22 , 35 ], with .…”

“…However, in some contexts, it is not possible to obtain long time series [ 22 ], or for decisions have to be made as quickly as possible, once a few samples are already available for analysis [ 21 ] in a real time system. In addition, long records are more likely to exhibit changes in the underlying dynamics.…”

Slope Entropy: A New Time Series Complexity Estimator Based on Both Symbolic Patterns and Amplitude Information

“…It is also important to note that SlopEn was applied to not normalised records, with a great disparity in amplitudes. For optimal performance, a grid search could be conducted [ 14 ], and after a normalisation process, it could also be found out which parameter values are optimal for each dataset. Thus, further studies will be required to fine tune the use of the parameters, the number of thresholds, and to define a more uniform scheme using signal normalisation.…”

“…They are usually considered stationary, but in reality, they might exhibit some temporal changes. For example, border effects are quite common in biomedical records [ 14 ], and this impacts the results in a length influence analysis. Other well–known effects are the stochastic resonance [ 64 , 65 ], whereby more noise does not necessary imply less discriminating power, just the opposite.…”

“…Since the introduction of ApEn, other derived methods have been proposed, such as Sample Entropy (SampEn) [ 9 ], Fuzzy Entropy (FuzzyEn) [ 10 ], Quadratic Sample Entropy (QSE) [ 11 ], and many more based on counting pattern matches in terms of time series subsequences amplitude differences. In addition, characterization studies related to these measures have also been published in order to avoid blind application of such methods or to maximise the performance achieved [ 12 , 13 , 14 , 15 , 16 ]. The concomitant analysis of time series at different temporal scales is also a frequent strategy in practically any method to gain a better insight into temporal dynamics [ 17 , 18 ].…”