NDLI: Detection of Correct and Incorrect Measurements in Real-Time Continuous Glucose Monitoring Systems by Applying a Postprocessing Support Vector Machine

Content Provider	IEEE Xplore Digital Library
Author	Leal, Y. Gonzalez-Abril, L. Lorencio, C. Bondia, J. Vehi, J.
Copyright Year	1964
Abstract	Support vector machines (SVMs) are an attractive option for detecting correct and incorrect measurements in real-time continuous glucose monitoring systems (RTCGMSs), because their learning mechanism can introduce a postprocessing strategy for imbalanced datasets. The proposed SVM considers the geometric mean to obtain a more balanced performance between sensitivity and specificity. To test this approach, 23 critically ill patients receiving insulin therapy were monitored over 72 h using an RTCGMS, and a dataset of 537 samples, classified according to International Standards Organization (ISO) criteria (372 correct and 165 incorrect measurements), was obtained. The results obtained were promising for patients with septic shock or with sepsis, for which the proposed system can be considered as reliable. However, this approach cannot be considered suitable for patients without sepsis.
Sponsorship	IEEE Engineering in Medicine and Biology Society
Page Count	9
File Size	1153020
Starting Page	1891
Ending Page	1899
File Format	PDF
ISSN	00189294
Volume Number	60
Issue Number	7
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-01-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines Sugar Accuracy Insulin Standards Monitoring support vector machines (SVMs) Balanced performance continuous glucose monitoring critically ill patients fault detection
Content Type	Text
Resource Type	Article
Subject	Biomedical Engineering

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