Early Detection of Hypoglycemia Events Based on Biometric Sensors Prototyped on FPGAs

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• 关键词：E ; health platforms ; FPGAs ; Biometric sensors ; Continuous Glucose Monitoring ; Diabetes
• 刊名：Lecture Notes in Computer Science
• 出版年：2016
• 出版时间：2016
• 年：2016
• 卷：10069
• 期：1
• 页码：133-145
• 全文大小：500 KB
• 参考文献：1.Annual Report 2012: International Diabetes Federation (2012). http://​www.​idf.​org
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  11.Tomasello, A.: Incidencia de Hipoglucemias en DM2 mayores de 60 años medidas a través de monitoreo glucémico continuo y su relación con estilo de vida y tratamiento de Diabetes. Ph.D. thesis, Fundación H.A. Barceló (2014)
  12.Vashist, S.K.: Continuous glucose monitoring systems: a review. Diagnostics 3(4), 385–412 (2013). http://​www.​mdpi.​com/​2075-4418/​3/​4/​385
  
• 作者单位：Soledad Escolar (17)
  Manuel J. Abaldea (18)
  Julio D. Dondo (18)
  Fernando Rincón (18)
  Juan Carlos López (18)
  
  17. Institute of Technology and Information Systems, Ciudad Real, Spain
  18. School of Computing Science, University of Castilla-La Mancha, Ciudad Real, Spain
  
• 丛书名：Ubiquitous Computing and Ambient Intelligence
• ISBN：978-3-319-48746-5
• 刊物类别：Computer Science
• 刊物主题：Artificial Intelligence and Robotics
  Computer Communication Networks
  Software Engineering
  Data Encryption
  Database Management
  Computation by Abstract Devices
  Algorithm Analysis and Problem Complexity
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1611-3349
• 卷排序：10069

文摘

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.