Abstract
We propose two ideas of how recorded signals from continuous glucose monitoring systems could be used to derive information about the patient-specific characteristics of the glucose dynamics for individuals suffering from type 1 diabetes and how these characteristics of the glucose dynamics could be linked to basic patient data (sex, age,...). Ultimately, these relationships could be used in the future in order to classify patients based on these basic patient data. In the first approach a simple transfer function model was used to fit recorded signals from continuous glucose monitoring systems. Using this approach on data from a recent clinical study, a statistically significant relationship between the model parameters and sex, body mass index, weight and age of the corresponding patients could be identified. The observed relationships could be verified with findings in the clinical studies that were documented in the previous publications. In the second approach a moving average filter with a varying filter width was applied on the data and the variance between filtered and unfiltered signal as a function of the filter width was analysed. From the analysed data a relationship between the low blood glucose index and the high frequency content of signals from continuous glucose monitoring systems seems likely.
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Acknowledgments
The authors gratefully acknowledge the sponsoring of this work by the COMET K2 center “Austrian Center of Competence in Mechatronics (ACCM)”. The COMET Program is funded by the Austrian federal government, the federal state Upper Austria and the scientific partners of ACCM.
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Reiterer, F., Kirchsteiger, H., Freckmann, G., del Re, L. (2016). Can We Use Measurements to Classify Patients Suffering from Type 1 Diabetes into Subcategories and Does It Make Sense?. In: Kirchsteiger, H., Jørgensen, J., Renard, E., del Re, L. (eds) Prediction Methods for Blood Glucose Concentration. Lecture Notes in Bioengineering. Springer, Cham. https://doi.org/10.1007/978-3-319-25913-0_4
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DOI: https://doi.org/10.1007/978-3-319-25913-0_4
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