Top

Published in:

2020 | OriginalPaper | Chapter

Handling Missing Data in CGM Records

Authors : Sara Zulj, Paulo Carvalho, Rogerio Ribeiro, Ratko Magjarevic

Published in: Future Trends in Biomedical and Health Informatics and Cybersecurity in Medical Devices

Publisher: Springer International Publishing

Activate our intelligent search to find suitable subject content or patents.

search-config

AI-assisted search

Off

Abstract

Continuous glucose monitor (CGM) is a wearable body sensor device that automatically measures glucose at regular intervals. CGM records can be used to analyze data after the collection or for predicting certain trends in the records in real-time. Nevertheless, missing values are common in CGM records. In this paper we discuss possible methods to replace the missing values in short-length gaps, in both real-time and after the collection use.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

previous chapter Comparison of Human Fall Acceleration Signals Among Different Datasets

next chapter Based on DICOM RT Structure and Multiple Loss Function Deep Learning Algorithm in Organ Segmentation of Head and Neck Image

Klonoff, D.C., Ahn, D., Drincic, A.: Continuous glucose monitoring: a review of the technology and clinical use. Diabetes Res. Clin. Pract. 133, 178–192 (2017). https://doi.org/10.1016/j.diabres.2017.08.005CrossRef

Mariani, H.S., Layden, B.T., Aleppo, G.: Continuous glucose monitoring: a perspective on its past, present, and future applications for diabetes management. Clin. Diabetes 35(1), 60–65 (2017). https://doi.org/10.2337/cd16-0008CrossRef

Schmelzeisen-Redeker, G., Schoemaker, M., Kirchsteiger, H., Freckmann, G., Heinemann, L., del Re, L.: Time delay of CGM sensors. J. Diabetes Sci. Technol. 9(5), 1006–1015 (2015). https://doi.org/10.1177/1932296815590154CrossRef

Kruger, D.F., Edelman, S.V., Hinnen, D.A., Parkin, C.G.: Reference guide for integrating continuous glucose monitoring into clinical practice. Diab. Educ. (2018). https://doi.org/10.1177/0145721718818066CrossRef

Danne, T., et al.: International consensus on use of continuous glucose monitoring. Diabetes. Care 40(12), 1631–1640 (2017). https://doi.org/10.2337/dc17-1600CrossRef

Fantasia, K., Modzelewski, K., Steenkamp, D: Predictive glucose trends from continuous glucose monitoring: friend or foe in clinical decision making? J. Diabetes Sci. Technol. (2019). https://doi.org/10.1177/1932296818823538CrossRef

Ajjan, R.A., Cummings, M.H., Jennings, P., Leelarathna, L., Rayman, G., Wilmot, E.G.: Optimising use of rate-of-change trend arrows for insulin dosing decisions using the FreeStyle Libre flash glucose monitoring system. Diab. Vasc. Dis. Res. (2018). https://doi.org/10.1177/1479164118795252CrossRef

Serrà, J., Arcos, J.L.: An empirical evaluation of similarity measures for time series classification. Knowl. Based Syst. 67, 305–314 (2014). https://doi.org/10.1016/j.knosys.2014.04.035CrossRef

Willmott, C., Matsuura, K.: Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance. Climate Res. 30, 79–82 (2005). https://doi.org/10.3354/cr030079CrossRef

Title: Handling Missing Data in CGM Records
Authors: Sara Zulj
Paulo Carvalho
Rogerio Ribeiro
Ratko Magjarevic
Publisher: Springer International Publishing
Book: Future Trends in Biomedical and Health Informatics and Cybersecurity in Medical Devices
Print ISBN: 978-3-030-30635-9

Electronic ISBN: 978-3-030-30636-6

Copyright Year: 2020
DOI: https://doi.org/10.1007/978-3-030-30636-6_57

Springer Professional

Abstract

Please log in to get access to your license.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"