Abstract
Diabetes is a chronic disease that requires continuous medical care and patient self-monitoring processes. The control of the glucose level in blood is a task that the patient needs to perform to prevent hypoglycemia episodes. Early detection of hypoglycemia is a very important element for preventing multi-organ failure. The incorporation of other biomedical parameters monitoring, combined with glucose levels can help to early detect and prevent those episodes. At this respect, several e-health platforms have been developed for monitoring and processing vital signals related to diabetes events. In this paper we evaluate a couple of these platforms and we introduce an algorithm to analyze the data of glucose, in order to anticipate the moment of an hypoglycemia episode. The proposed algorithm contemplates the information of several biomedical sensors, and it is based on the analysis of the gradient of the glucose curve, producing an estimation of the expected time to achieve a given threshold. Besides, the proposed algorithm allows to analyze the correlations of the monitored multi-signals information with diabetes related events. The algorithm was developed to be implemented on an FPGA-based SoC and was evaluated by simulation. The results obtained are very promising and can be scalable to further signals processing.
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Acknowledgments
This work has been funded by the Programme for Research and Innovation of University of Castilla-La Mancha, co-financed by the European Social Fund (Resolution of 25 August 2014) and by the Spanish Ministry of Economy and Competitiveness under project REBECCA (TEC2014-58036-C4-1-R) and the Regional Government of Castilla-La Mancha under project SAND (PEII_2014_046_P).
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Escolar, S., Abaldea, M.J., Dondo, J.D., Rincón, F., López, J.C. (2016). Early Detection of Hypoglycemia Events Based on Biometric Sensors Prototyped on FPGAs. In: García, C., Caballero-Gil, P., Burmester, M., Quesada-Arencibia, A. (eds) Ubiquitous Computing and Ambient Intelligence. UCAmI 2016. Lecture Notes in Computer Science(), vol 10069. Springer, Cham. https://doi.org/10.1007/978-3-319-48746-5_14
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DOI: https://doi.org/10.1007/978-3-319-48746-5_14
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