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Open AccessArticle

Glucose Prediction under Variable-Length Time-Stamped Daily Events: A Seasonal Stochastic Local Modeling Framework

1
Instituto Universitario de Automática e Informática Industrial, Universitat Politècnica de València, Camino de Vera, s/n, 46022 València, Spain
2
Centro de Investigación Biomédica en Red de Diabetes y Enfermedades Metabólicas Asociadas (CIBERDEM), Instituto de Salud Carlos III, 28029 Madrid, Spain
*
Author to whom correspondence should be addressed.
Academic Editor: Andrea Facchinetti
Sensors 2021, 21(9), 3188; https://doi.org/10.3390/s21093188
Received: 20 March 2021 / Revised: 23 April 2021 / Accepted: 28 April 2021 / Published: 4 May 2021
(This article belongs to the Special Issue Recent Advances in Continuous Glucose Monitoring Sensors)
Accurate glucose prediction along a long-enough time horizon is a key component for technology to improve type 1 diabetes treatment. Subjects with diabetes might benefit from supervision and control systems that accurately predict risks and trigger corrective actions early enough with improved mitigation. However, large intra-patient variability poses big challenges to glucose prediction. In previous works by the authors, clustering and local modeling techniques with seasonal stochastic models proved to be efficient, allowing for good glucose prediction accuracy for long prediction horizons. Continuous glucose monitoring (CGM) data were partitioned into fixed-length postprandial time subseries and clustered with Fuzzy C-Means to collect similar behaviors, enforcing seasonality at each cluster after subseries concatenation. Then, seasonal stochastic models were identified for each cluster and local predictions were integrated into a global prediction. However, free-living conditions do not support the fixed-length partition of CGM data since daily events duration is variable. In this work, a new algorithm is provided to overcome this constraint, allowing better coping with patient’s variability under variable-length time-stamped daily events in supervision and control applications. Besides predicted glucose, two real-time indices are additionally provided—a crispness index, indicating good representation of current glucose behavior by a single model, and a normality index, allowing for the detection of an abnormal glucose behavior (unusual according to registered historical data). The framework is tested in a proof-of-concept in silico study with ten patients over four month training data and two independent two month validation datasets, with and without abnormal behaviors, from the distribution version of the UVA/Padova simulator extended with diverse sources of intra-patient variability. View Full-Text
Keywords: type 1 diabetes; glucose prediction; seasonal local models; Fuzzy C-Means type 1 diabetes; glucose prediction; seasonal local models; Fuzzy C-Means
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MDPI and ACS Style

Montaser, E.; Díez, J.-L.; Bondia, J. Glucose Prediction under Variable-Length Time-Stamped Daily Events: A Seasonal Stochastic Local Modeling Framework. Sensors 2021, 21, 3188. https://doi.org/10.3390/s21093188

AMA Style

Montaser E, Díez J-L, Bondia J. Glucose Prediction under Variable-Length Time-Stamped Daily Events: A Seasonal Stochastic Local Modeling Framework. Sensors. 2021; 21(9):3188. https://doi.org/10.3390/s21093188

Chicago/Turabian Style

Montaser, Eslam; Díez, José-Luis; Bondia, Jorge. 2021. "Glucose Prediction under Variable-Length Time-Stamped Daily Events: A Seasonal Stochastic Local Modeling Framework" Sensors 21, no. 9: 3188. https://doi.org/10.3390/s21093188

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