Filters








1,207 Hits in 9.5 sec

Real-Time Glucose Estimation Algorithm for Continuous Glucose Monitoring Using Autoregressive Models

Y. Leal, W. Garcia-Gabin, J. Bondia, E. Esteve, W. Ricart, J.-M. Fernandez-Real, J. Vehi
2010 Journal of Diabetes Science and Technology  
Continuous glucose monitors (CGMs) present a problem of lack of accuracy, especially in the lower range, sometimes leading to missed or false hypoglycemia.  ...  Results: A total of 563 paired points were obtained from BG and monitor readings to validate the new algorithm. 98.5% of paired points fell in zones A+B of the Clarke error grid analysis with the proposed  ...  Acknowledgements: Yenny Leal acknowledges the BR Grants of the University of Girona.  ... 
doi:10.1177/193229681000400221 pmid:20307401 pmcid:PMC2864176 fatcat:adph7s64bvfejdwfwgu6llxwxu

Continuous Glucose Monitoring Sensors: Past, Present and Future Algorithmic Challenges

Andrea Facchinetti
2016 Sensors  
CGM sensors have revolutionized Type 1 diabetes (T1D) management, improving glucose control when used adjunctively to self-monitoring blood glucose systems.  ...  Continuous glucose monitoring (CGM) sensors are portable devices that allow measuring and visualizing the glucose concentration in real time almost continuously for several days and are provided with hypo  ...  blood glucose (BG) values collected using a portable self-monitoring BG (SMBG) device.  ... 
doi:10.3390/s16122093 pmid:27941663 pmcid:PMC5191073 fatcat:4yxftwnybvfrnec67cv4nyo4nu

A Model of Self-Monitoring Blood Glucose Measurement Error

Martina Vettoretti, Andrea Facchinetti, Giovanni Sparacino, Claudio Cobelli
2017 Journal of Diabetes Science and Technology  
A reliable model of the probability density function (PDF) of self-monitoring of blood glucose (SMBG) measurement error would be important for several applications in diabetes, like testing in silico insulin  ...  Methods: The blood glucose range is divided into zones where error (absolute or relative) presents a constant standard deviation (SD).  ...  based on self-monitoring of blood glucose (SMBG) measurements, collected in a small drop of capillary blood by portable BG meters. 1 SMBG measurements are affected by measurement error which can negatively  ... 
doi:10.1177/1932296817698498 pmid:28299958 pmcid:PMC5588839 fatcat:sjebwnfvrzaq5jy4yelwfcgvbq

Overcoming Individual Discrepancies, a Learning Model for Non-Invasive Blood Glucose Measurement

Weijie Liu, Anpeng Huang, Ping Wan
2019 Applied Sciences  
Non-invasive Glucose Measurement (NGM) technology makes great sense for the blood glucose management of patients with hyperglycemia or hypoglycemia.  ...  In this paper, an NGM prototype is designed, and a learning model for glucose estimating with automatically parameters tuning based on Independent Component Analysis (ICA) and Random Forest (RF) is presented  ...  Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/app9010192 fatcat:lo6qpe6azzelfkckdutf6oemzi

Offline and online data assimilation for real-time blood glucose forecasting in type 2 diabetes [article]

Matthew E Levine, George Hripcsak, Lena Mamykina, Andrew Stuart, David J Albers
2017 arXiv   pre-print
We evaluate the benefits of combining different offline and online data assimilation methodologies to improve personalized blood glucose prediction with type 2 diabetes self-monitoring data.  ...  We fit a model of ultradian glucose dynamics to the first half of each data set using offline (MCMC and nonlinear optimization) and online (unscented Kalman filter and an unfiltered model---a dynamical  ...  Conclusions We advance our understanding of the impact of different inference methodologies on blood glucose forecasting on type 2 diabetes self-monitoring data under a single, ultradian model of glucose  ... 
arXiv:1709.00163v1 fatcat:mzufryeo7zhwhafr4lkv3ytq7y

Convolutional Recurrent Neural Networks for Glucose Prediction [article]

Kezhi Li, John Daniels, Chengyuan Liu, Pau Herrero, Pantelis Georgiou
2019 arXiv   pre-print
Control of blood glucose is essential for diabetes management.  ...  In this work, we present a deep learning model that is capable of forecasting glucose levels with leading accuracy for simulated patient cases (RMSE = 9.38±0.71 [mg/dL] over a 30-minute horizon, RMSE =  ...  The standard approach to diabetes management requires the subject to actively undertake blood glucose measurements a handful of times throughout the day with a finger prick test -self monitoring of blood  ... 
arXiv:1807.03043v5 fatcat:u6obvkbjvzg2ba2ictvyu7bu3u

The Progress of Glucose Monitoring—A Review of Invasive to Minimally and Non-Invasive Techniques, Devices and Sensors

Wilbert Villena Gonzales, Ahmed Mobashsher, Amin Abbosh
2019 Sensors  
Current glucose monitoring methods for the ever-increasing number of diabetic people around the world are invasive, painful, time-consuming, and a constant burden for the household budget.  ...  The review concludes that the adoption and use of new technologies for glucose detection is unavoidable and closer to become a reality.  ...  Acknowledgments: We would like to acknowledge Robert Bird and Karl Bertling for their guidance and advice on glucose detection and spectroscopy techniques.  ... 
doi:10.3390/s19040800 fatcat:ztuzmn2tirdejdzjwcgicfdhju

Limits to the Evaluation of the Accuracy of Continuous Glucose Monitoring Systems by Clinical Trials

Patrick Schrangl, Florian Reiterer, Lutz Heinemann, Guido Freckmann, Luigi del Re
2018 Biosensors  
This accuracy is usually determined with clinical studies by comparing the data obtained by the given CGM system with blood glucose (BG) point measurements made with a so-called reference method.  ...  Systems for continuous glucose monitoring (CGM) are evolving quickly, and the data obtained are expected to become the basis for clinical decisions for many patients with diabetes in the near future.  ...  Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/bios8020050 pmid:29783669 pmcid:PMC6023102 fatcat:2fk3jhnuwbctnlyakxbe6zl6ie

A Novel Adaptive-Weighted-Average Framework for Blood Glucose Prediction

Youqing Wang, Xiangwei Wu, Xue Mo
2013 Diabetes Technology & Therapeutics  
Blood glucose (BG) prediction plays a very important role in daily BG management of patients with diabetes mellitus.  ...  For each patient, the algorithms were evaluated in terms of root-mean-square error, relative error, Clarke error-grid analysis, and J index.  ...  These therapies can be optimized by self-monitoring blood glucose (BG) approximately three or four times per day.  ... 
doi:10.1089/dia.2013.0104 pmid:23883406 pmcid:PMC3781119 fatcat:hzb52cm6pnbzdfxm72vsm2sof4

Diabetes Technology: Markers, Monitoring, Assessment, and Control of Blood Glucose Fluctuations in Diabetes

Boris P. Kovatchev
2012 Scientifica  
Continuous glucose monitoring (CGM) was introduced in 1999 and has evolved from means for retroactive review of blood glucose profiles to versatile reliable devices, which monitor the course of glucose  ...  Diabetes technology has progressed remarkably over the past 50 years—a progress that includes the development of markers for diabetes control, sophisticated monitoring techniques, mathematical models,  ...  e author thanks his colleagues at the University of Virginia Center for Diabetes Technology for their relentless work on arti�cial pancreas development.  ... 
doi:10.6064/2012/283821 pmid:24278682 pmcid:PMC3820631 fatcat:luecofi7hvd2pmb37jsn53f224

Using Contextual Information to Improve Blood Glucose Prediction [article]

Mohammad Akbari, Rumi Chunara
2019 arXiv   pre-print
Therefore, here we propose a Gaussian Process model to both address these data challenges and combine blood glucose and latent feature representations of contextual data for a novel multi-signal blood  ...  Given a robust evaluation across two blood glucose datasets with different forms of contextual information, we conclude that multi-signal Gaussian Processes can improve blood glucose prediction by using  ...  This has included accuracy of the sensors, delay of insulin action and glucose level estimation by the continuous glucose monitoring (CGM) system, and the lack of models that account for social, contextual  ... 
arXiv:1909.01735v1 fatcat:jt27zxuwi5d6zjw7btqvkjlkjy

"Smart" Continuous Glucose Monitoring Sensors: On-Line Signal Processing Issues

Giovanni Sparacino, Andrea Facchinetti, Claudio Cobelli
2010 Sensors  
The availability of continuous glucose monitoring (CGM) sensors allows development of new strategies for the treatment of diabetes.  ...  Thirdly, predictions of future glucose concentration should be generated with suitable modeling methodologies.  ...  Acknowledgements Some of the algorithms for calibration, denoising, prediction and alert generation discussed in this paper have been deposited by the University of Padova [39, 45] .  ... 
doi:10.3390/s100706751 pmid:22163574 pmcid:PMC3231130 fatcat:55pzgmjhjjfcpd52iqrrsd3kau

Convolutional Recurrent Neural Networks for Glucose Prediction

Kezhi Li, John Daniels, Chengyuan Liu, Pau Herrero-Vinas, Pantelis Georgiou
2019 IEEE journal of biomedical and health informatics  
In this paper, we present a deep learning model that is capable of forecasting glucose levels with leading accuracy for simulated patient cases (root-mean-square error (RMSE) = 9.38 ± 0.71 [mg/dL] over  ...  Control of blood glucose is essential for diabetes management.  ...  The standard approach to diabetes management requires people actively taking BG measurements a handful of times throughout the day with a finger prick test -self monitoring of blood glucose.  ... 
doi:10.1109/jbhi.2019.2908488 pmid:30946685 fatcat:ns5m543j2zbs3g2yeskcc5sebm

Blood Glucose Prediction with Variance Estimation Using Recurrent Neural Networks

John Martinsson, Alexander Schliep, Björn Eliasson, Olof Mogren
2019 Journal of Healthcare Informatics Research  
We evaluate our method using the standard root-mean-squared error (RMSE) metric, along with a blood glucose-specific metric called the surveillance error grid (SEG).  ...  Modern continuous glucose monitoring systems provide excellent sources of data to train machine learning models to predict future glucose levels.  ...  Acknowledgments Open access funding provided by RISE Research Institutes of Sweden.  ... 
doi:10.1007/s41666-019-00059-y pmid:35415439 pmcid:PMC8982803 fatcat:h2vm2ttczvfddlekcxqmwqrcvu

Neural Physiological Model: A Simple Module for Blood Glucose Prediction

Kang Gu, Ruoqi Dang, Temiloluwa Prioleau
2020 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)  
For predicting blood glucose 30-mins in advance, NPE+LSTM yields an average root mean square error (RMSE) of 9.18 mg/dL on an in-house diabetes dataset from 34 subjects.  ...  neural network for blood glucose prediction.  ...  The authors would like to thank Tidepool for their support and contribution which makes this research possible.  ... 
doi:10.1109/embc44109.2020.9176004 pmid:33019219 fatcat:67s3yxupubalvcowzexbcntliy
« Previous Showing results 1 — 15 out of 1,207 results