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Title Prediction methods for blood glucose concentration : design, use and evaluation / Harald Kirchsteiger, John Bagterp Jrgensen, Eric Renard, Luigi del Re editors.
Imprint Cham : Springer, [2016]

View online
View online
Series Lecture notes in bioengineering
Lecture notes in bioengineering.
Subject Blood sugar monitoring -- Equipment and supplies.
Blood sugar -- Analysis.
Blood Glucose Self-Monitoring.
Blood Glucose -- analysis.
Alt Name Kirchsteiger, Harald,
Jrgensen, John Bagterp,
Renard, Eric,
Del Re, Luigi,
Description 1 online resource.
polychrome rdacc
Bibliography Note Includes bibliographical references.
Contents Preface; Contents; Alternative Frameworks for Personalized Insulin -- Glucose Models; 1 Introduction; 2 Alternatives for Modeling; 3 Model Structures; 4 Interval Models; 4.1 Continuous Time System Identification; 4.2 Interval Model Results; 5 A Probabilistic Approach; 5.1 Gaussian and Generalized Gaussian Mixture Models; 5.2 Modeling Method and Model Structure; 5.3 Modeling Results; 6 Conclusion and Outlook; References; Accuracy of BG Meters and CGM Systems: Possible Influence Factors for the Glucose Prediction Based on Tissue Glucose Concentrations; 1 Introduction.
2 SMBG Accuracy and CGM Calibration with SMBG Results2.1 SMBG Accuracy; 2.2 CGM Calibration with SMBG Results; 3 Accuracy of CGM Systems; 3.1 Mean Absolute Relative Difference; 3.2 Precision Absolute Relative Difference; 4 Glucose Prediction Based on Tissue Glucose Concentrations; References; CGM -- How Good Is Good Enough?; 1 Background; 2 CGM Performance Assessment; 2.1 Sensor Signal; 2.2 Reference Methodology; 2.3 Accuracy and Precision; 3 State of the Art; 4 Unresolved Issues; 4.1 Transient Sensor Signal Disruption; 4.2 Transient Significant CGM Inaccuracies.
5 Next Steps in CGM Development6 Conclusion; References; Can We Use Measurements to Classify Patients Suffering from Type 1 Diabetes into Subcategories and Does It Make Sense?; 1 Introduction; 2 Database of CGMS Recordings; 3 Modelling Using a Simple Transfer Function Model; 3.1 Description of the Model and System Identification; 3.2 Trends and Correlations; 3.3 Clustering and Classification; 3.4 Discussion of Results and Further Outlook; 4 Analysis of the High Frequency Content of CGMS Signals; 4.1 Filtering of CGMS Signals; 4.2 Trends and Classification.
4.3 Discussion of Results and Further OutlookReferences; Prevention of Severe Hypoglycemia by Continuous EEG Monitoring; 1 Background; 2 Clinical Studies -- Proof of Concept; 3 The Device; 4 Quantitative Evaluation of EEG Recorded with the Partly Implanted EEG Recorder; 5 Development of an Algorithm for Detection and Warning of Severe Hypoglycaemia in Type 1 Diabetes; 6 Clinical Studies -- Preliminary Results with Implanted Device; 7 Discussion and Perspectives; 8 Conclusion; References; Meta-Learning Based Blood Glucose Predictor for Diabetic Smartphone App; 1 Introduction.
2 Fully Adaptive Regularized Learning Algorithm for the Blood Glucose Prediction3 Android Version of the FARL Algorithm; 3.1 Translation of the Algorithm from Matlab to Android System; 3.2 Microprocessor and Power Consumption Analysis; 4 Performance Assessment; 4.1 Clinical Accuracy Metrics; 4.2 Performance Assessment; 4.3 Comparison of the Matlab and Android Versions; 5 Conclusions and Discussion; References; Predicting Glycemia in Type 1 Diabetes Mellitus with Subspace-Based Linear Multistep Predictors; 1 Introduction; 2 Subspace-Based Linear Multistep Predictors; 2.1 Notation.
Summary This book tackles the problem of overshoot and undershoot in blood glucose levels caused by delay in the effects of carbohydrate consumption and insulin administration. The ideas presented here will be very important in maintaining the welfare of insulin-dependent diabetics and avoiding the damaging effects of unpredicted swings in blood glucose - accurate prediction enables the implementation of counter-measures. The glucose prediction algorithms described are also a key and critical ingredient of automated insulin delivery systems, the so-called "artificial pancreas". The authors address the topic of blood-glucose prediction from medical, scientific and technological points of view. Simulation studies are utilized for complementary analysis but the primary focus of this book is on real applications, using clinical data from diabetic subjects. The text details the current state of the art by surveying prediction algorithms, and then moves beyond it with the most recent advances in data-based modeling of glucose metabolism. The topic of performance evaluation is discussed and the relationship of clinical and technological needs and goals examined with regard to their implications for medical devices employing prediction algorithms. Practical and theoretical questions associated with such devices and their solutions are highlighted. This book shows researchers interested in biomedical device technology and control researchers working with predictive algorithms how incorporation of predictive algorithms into the next generation of portable glucose measurement can make treatment of diabetes safer and more efficient.
Note Vendor-supplied metadata.
ISBN 9783319259130 (electronic bk.)
331925913X (electronic bk.)
3319259113 (print)
9783319259116 (print)
ISBN/ISSN 10.1007/978-3-319-25913-0
OCLC # 930600988
Additional Format Print version: Kirchsteiger, Harald. Prediction Methods for Blood Glucose Concentration : Design, Use and Evaluation. Cham : Springer International Publishing, 2015 9783319259116

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