Early Detection of Hypoglycemia Events Based on Biometric Sensors Prototyped on FPGAs
Computer Architecture and Networks Group, University of Castilla-La Mancha, Ciudad Real, Spain
soledad.escolar@uclm.es
Computer Architecture and Networks Group, University of Castilla-La Mancha, Ciudad Real, Spain
manueljose.abaldea@uclm.es
Computer Architecture and Networks Group, University of Castilla-La Mancha, Ciudad Real, Spain
julio.dondo@uclm.es
Computer Architecture and Networks Group, University of Castilla-La Mancha, Ciudad Real, Spain
fernando.rincon@uclm.es
Computer Architecture and Networks Group, University of Castilla-La Mancha, Ciudad Real, Spain
juancarlos.lopez@uclm.es
CONFERENCE — International Conference on Ubiquitous Computing and Ambient Intelligence
PAGES — 1-12
VOLUME — 10069
LOCATION — Gran Canaria, Spain
ISSN — 978-3-319-48746-5
PUBLISHER — Springer
YEAR — 2016
@inproceedings{escolar2016early,
title={Early Detection of Hypoglycemia Events Based on Biometric Sensors Prototyped on FPGAs},
author={Escolar, Soledad and Abaldea, Manuel J and Dondo, Julio D and Rinc{\'o}n, Fernando and L{\'o}pez, Juan Carlos},
booktitle={Ubiquitous Computing and Ambient Intelligence: 10th International Conference, UCAmI 2016, San Bartolom{\'e} de Tirajana, Gran Canaria, Spain, November 29--December 2, 2016, Proceedings, Part I 10},
pages={133--145},
year={2016},
organization={Springer}
}
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.
