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Cited by 62Diabetes Management and ResearchFault Detection and Control SystemsDiabetes Management and Education

An Online Failure Detection Method of the Glucose Sensor-Insulin Pump System: Improved Overnight Safety of Type-1 Diabetic Subjects

Facchinetti, Andrea, Favero, Simone, Sparacino, Giovanni, Cobelli, Claudio·2013·IEEE Transactions on Biomedical Engineering
DOI 10.1109/tbme.2012.2227256
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Abstract

Sensors for real-time continuous glucose monitoring (CGM) and pumps for continuous subcutaneous insulin infusion (CSII) have opened new scenarios for Type-1 diabetes treatment. However, occasional failures of either CGM or CSII may expose diabetic patients to possibly severe risks, especially overnight (e.g., inappropriate insulin administration). In this contribution, we present a method to detect in real time such failures by simultaneously using CGM and CSII data streams and a black-box model of the glucose-insulin system. First, an individualized state-space model of the glucose-insulin system is identified offline from CGM and CSII data collected during a previous monitoring. Then, this model, CGM and CSII real-time data streams are used online to obtain predictions of future glucose concentrations together with their confidence intervals by exploiting a Kalman filtering approach. If glucose values measured by the CGM sensor are not consistent with the predictions, a failure alert is generated in order to mitigate the risks for patient safety. The method is tested on 100 virtual patients created by using the UVA/Padova Type-1 diabetic simulator. Three different types of failures have been simulated: spike in the CGM profile, loss of sensitivity of glucose sensor, and failure in the pump delivery of insulin. Results show that, in all cases, the method is able to correctly generate alerts, with a very limited number of false negatives and a number of false positives, on av…

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  • There was a single false positive alert in one scenario after 1,257 hours of nonfaulty simulation.

    “There was a single false positive alert in one scenario after 1,257 hours of nonfaulty simulation.15”

    From Insulin Pump Occlusions: For Patients Who Have Been Around the (Infusion) Block (2017) · cites this paper as [15]

  • With a predefined upper safety limit for blood glucose of 300 mg/dl, all 100 in silico scenarios detected the fault below the safety limit except for two false negatives.

    “With a predefined upper safety limit for blood glucose of 300 mg/dl all 100 scenarios detected the fault below the safety limit except for two false negatives.”

    From Insulin Pump Occlusions: For Patients Who Have Been Around the (Infusion) Block (2017) · cites this paper as [15]

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Authors
Facchinetti, A., Favero, S., Sparacino, G., & Cobelli, C.
Affiliations
University of Padua (IT)
ORCID
Venue
IEEE Transactions on Biomedical Engineering
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Published
2013-02
Volume
60
Issue
2
Pages
406-416
ISSN
  • 0018-9294 (ISSN-L)
  • 1558-2531
DOI
10.1109/tbme.2012.2227256
OpenAlex
W1978587779
Cited by
62
References
7
Citations by year
2026: 1 · 2025: 4 · 2024: 9 · 2023: 3 · 2022: 1 · 2021: 5 · 2020: 5 · 2019: 4 · 2018: 7 · 2017: 7 · 2016: 6 · 2015: 1 · 2014: 5 · 2013: 4
FWCI
2.57
Type
journal article
Language
en
Open access
closed
Licence
Locations
  • IEEE Transactions on Biomedical Engineering (publishedVersion)
  • PubMed (publishedVersion)
  • Padua Research Archive (University of Padova) (submittedVersion)
Topics
  • Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes Management and Research
  • Physical Sciences › Engineering › Control and Systems Engineering › Fault Detection and Control Systems
  • Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes Management and Education
Keywords
fault detection, continuous glucose monitoring, insulin pump therapy, type 1 diabetes, glucose-insulin system, state-space model identification, Kalman filter, glucose prediction, confidence intervals, sensor faults, pump failure, UVA/Padova simulator, virtual patients
MeSH terms
Algorithms, Computer Simulation, Diabetes Mellitus, Type 1, Humans, Hypoglycemic Agents, Insulin, Insulin Infusion Systems, Sensitivity and Specificity, Signal Processing, Computer-Assisted, Blood Glucose Self-Monitoring, Reproducibility of Results, Equipment Failure Analysis

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