Hypoglycemia Detection and Prediction Using Continuous Glucose Monitoring—A Study on Hypoglycemic Clamp Data

Palerm, Cesar C.; Bequette, B. Wayne

doi:10.1177/193229680700100505

Cited by 83 publications

(75 citation statements)

References 11 publications

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“…They applied the algorithm to predict hypoglycemia using data from a series of hypoglycemic clamps in which a CGMS® System (Medtronic-Minimed, Northridge, CA) was employed [13]. For 30 minutes of PH and an alarm threshold of 70 mg/dl, the algorithm was effective in predicting hypoglycemia (sensitivity of 90% and specificity of 79%).…”

Section: Introductionmentioning

confidence: 99%

Artificial Neural Network Algorithm for Online Glucose Prediction from Continuous Glucose Monitoring

Pérez-Gandía

Facchinetti

Sparacino

et al. 2010

Diabetes Technology & Therapeutics

266

177

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Background and Aims: Continuous glucose monitoring (CGM) devices could be useful for real-time management of diabetes therapy. In particular, CGM information could be used in real-time to predict future glucose levels in order to prevent hypo/hyperglycemic events. This paper proposes a new on-line method for predicting future glucose concentration levels from CGM data. Methods:The predictor is implemented with an artificial neural network model (NNM). The inputs of the NNM are the values provided by the CGM sensor during the preceding 20 minutes, while the output is the prediction of glucose concentration at the chosen prediction horizon (PH) time. The method performance is assessed using datasets from two different CGM systems (9 subjects using the Medtronic Guardian and 6 subjects using the Abbott Navigator). Three different PH are used, i.e. 15, 30 and 45 minutes. The NNM accuracy has been estimated by using the root mean square error (RMSE) and prediction delay. Results:The RMSE is around 10, 18 and 27 mg/dl for 15, 30 and 45 minutes of PH, respectively. The prediction delay is around 4, 9 and 14 minutes for upward trends and 5, 15 and 26 minutes for downward trends, respectively. A comparison with a previously published technique, based on an autoregressive model (ARM) [1], has been performed. The comparison shows that, the proposed NNM is more accurate than the ARM, with no significant deterioration in the prediction delay. Conclusions:The proposed NNM is a reliable solution for the on-line prediction of future glucose concentrations from CGM data.

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Section: Introductionmentioning

confidence: 99%

Artificial Neural Network Algorithm for Online Glucose Prediction from Continuous Glucose Monitoring

Pérez-Gandía

Facchinetti

Sparacino

et al. 2010

Diabetes Technology & Therapeutics

266

177

View full text Add to dashboard Cite

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“…A hypoglycemic event was defined as at least 1 PG reading below 70 mg/dl. [13][14][15][16] A following period lasting a minimum of 30 minutes with no PG reading below 70 mg/dl was defined as the end of the event. For comparison with other former and future studies, a Clarke error grid analysis is presented in the results section.…”

Section: Data Processingmentioning

confidence: 99%

Evaluation of an Algorithm for Retrospective Hypoglycemia Detection Using Professional Continuous Glucose Monitoring Data

Jensen

Mahmoudi

Christensen

et al. 2014

J Diabetes Sci Technol

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For people with type 1 diabetes, tight glycemic control, accomplished by intensive insulin therapy, reduces the risk of developing late-diabetic complications. 1,2 The consequence of intensive insulin therapy is a 3-fold increase in the number of hypoglycemic events. These adverse effects are fatal in severe cases, and thus are of great concern, leading to potential lack of compliance with intensive insulin therapy. 3,4 Self-monitoring of blood glucose (SMBG) has become the standard approach of glucose monitoring. However, SMBG provides only a snapshot of the glycemic control throughout the day with typically 3-4 preprandial measurements. Consequently, SMBG misses postprandial, overnight, and other glucose variabilities, making it vulnerable as a method for identifying episodes of hypoglycemia. Professional continuous glucose monitoring (PCGM) technologies, on the other hand, measure interstitial glucose (IG) every 1-5 minutes and gives a much better insight into the patient's glucose profile. PCGM is prescribed to the patient so that he or she can monitor IG typically over a short course of 3-5 days. With such a high temporal glucose resolution, episodes of hypoglycemia could be identified by the patient in consultation with a clinician so as to develop a management strategy for adjusting glucose-regulating factors, such as insulin, carbohydrate intake, and exercise to avoid hypoglycemia. PCGM technology utilizes a sensor with a glucoseoxidase-enzyme-containing material placed in the subcutaneous tissue of the abdomen, thigh, or upper arm. Glucose 511744D STXXX10.

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“…[24][25][26][27][28] A following period of minimum 30 min with no PG readings below 70 mg/dl was defined as the end of the event.…”

Section: Subjects and Datamentioning

confidence: 99%

Professional Continuous Glucose Monitoring in Subjects with Type 1 Diabetes: Retrospective Hypoglycemia Detection

Jensen

Christensen

Tarnow

et al. 2013

J Diabetes Sci Technol

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Background: An important task in diabetes management is detection of hypoglycemia. Professional continuous glucose monitoring (CGM), which produces a glucose reading every 5 min, is a powerful tool for retrospective identification of unrecognized hypoglycemia. Unfortunately, CGM devices tend to be inaccurate, especially in the hypoglycemic range, which limits their applicability for hypoglycemia detection. The objective of this study was to develop an automated pattern recognition algorithm to detect hypoglycemic events in retrospective, professional CGM. Method: Continuous glucose monitoring and plasma glucose (PG) readings were obtained from 17 data sets of 10 type 1 diabetes patients undergoing insulin-induced hypoglycemia. The CGM readings were automatically classified into a hypoglycemic group and a nonhypoglycemic group on the basis of different features from CGM readings and insulin injection. The classification was evaluated by comparing the automated classification with PG using sample-based and event-based sensitivity and specificity measures. Results: With an event-based sensitivity of 100%, the algorithm produced only one false hypoglycemia detection. The sample-based sensitivity and specificity levels were 78% and 96%, respectively. Conclusions: The automated pattern recognition algorithm provides a new approach for detecting unrecognized hypoglycemic events in professional CGM data. The tool may assist physicians and diabetologists in conducting a more thorough evaluation of the diabetes patient's glycemic control and in initiating necessary measures for improving glycemic control.

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Hypoglycemia Detection and Prediction Using Continuous Glucose Monitoring—A Study on Hypoglycemic Clamp Data

Abstract: Sensitivity and specificity data as a function of prediction horizon and alarm threshold enable an individual to adjust the alarm to best meet their needs. Such decisions can be made depending on the subject's risk for hypoglycemia, for example.

Cited by 83 publications

References 11 publications

Artificial Neural Network Algorithm for Online Glucose Prediction from Continuous Glucose Monitoring

Artificial Neural Network Algorithm for Online Glucose Prediction from Continuous Glucose Monitoring

Evaluation of an Algorithm for Retrospective Hypoglycemia Detection Using Professional Continuous Glucose Monitoring Data

Professional Continuous Glucose Monitoring in Subjects with Type 1 Diabetes: Retrospective Hypoglycemia Detection

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