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A multistep algorithm for processing and calibration of microdialysis continuous glucose monitoring data.
Diabetes Technol Ther. 2013 Oct; 15(10):825-35.DT

Abstract

BACKGROUND

The deviation of continuous subcutaneous glucose monitoring (CGM) data from reference blood glucose measurements is substantial, and adequate signal processing is required to reduce the discrepancy between subcutaneous glucose and blood glucose values. The purpose of this study was to develop a multistep algorithm for the processing and calibration of continuous subcutaneous glucose monitoring data with high accuracy and short delay. Algorithm

PRESENTATION

The algorithm comprises three steps: rate-limiting filtering, selective smoothing, and robust calibration. Initially, the algorithm detects nonphysiological glucose rate-of-change and corrects it with a weighted local polynomial. Noisy signal parts that require smoothing are then detected based on zero crossing count of the sensor signal first-order differences, and an exponentially weighted moving average smooths the noisy parts of the signal afterward. Finally, calibration is performed using a first-order polynomial as the conversion function, with coefficients being estimated using robust regression with a bi-square weight function. ALGORITHM PERFORMANCE: The performance of the algorithm was evaluated on 16 patients with type 1 diabetes mellitus. To compare the algorithm with state-of-the-art CGM data denoising and calibration, the rate-limiting filter and selective smoothing were replaced with an adaptive Kalman filter, and the calibration method was replaced with the calibration algorithm presented in one of the Medtronic (Northridge, CA) CGM patents. The median (mean) of the absolute relative deviation (ARD) of the sensor glucose values processed by the newly developed algorithm from capillary reference blood glucose measurements was 14.8% (22.6%), 10.6% (14.6%), and 8.9% (11.7%) in hypoglycemia, euglycemia, and hyperglycemia, respectively, whereas for the alternative algorithm, the median (mean) was 22.2% (26.9%), 12.1% (15.9%), and 8.8 (11.3%), respectively. The median (mean) ARD in all ranges was 10.3% (14.7%) for the new algorithm and 11.5% (15.8%) for the alternative algorithm. The new algorithm had an average delay of 2.1 min across the patients, and the alternative algorithm had an average delay of 2.9 min.

CONCLUSIONS

The presented algorithm may increase the accuracy of CGM data.

Authors+Show Affiliations

1 Department of Health Science and Technology, Aalborg University , Aalborg, Denmark .No affiliation info availableNo affiliation info availableNo affiliation info available

Pub Type(s)

Comparative Study
Evaluation Study
Journal Article
Research Support, Non-U.S. Gov't
Validation Study

Language

eng

PubMed ID

23944955

Citation

Mahmoudi, Zeinab, et al. "A Multistep Algorithm for Processing and Calibration of Microdialysis Continuous Glucose Monitoring Data." Diabetes Technology & Therapeutics, vol. 15, no. 10, 2013, pp. 825-35.
Mahmoudi Z, Dencker Johansen M, Christiansen JS, et al. A multistep algorithm for processing and calibration of microdialysis continuous glucose monitoring data. Diabetes Technol Ther. 2013;15(10):825-35.
Mahmoudi, Z., Dencker Johansen, M., Christiansen, J. S., & Hejlesen, O. K. (2013). A multistep algorithm for processing and calibration of microdialysis continuous glucose monitoring data. Diabetes Technology & Therapeutics, 15(10), 825-35. https://doi.org/10.1089/dia.2013.0041
Mahmoudi Z, et al. A Multistep Algorithm for Processing and Calibration of Microdialysis Continuous Glucose Monitoring Data. Diabetes Technol Ther. 2013;15(10):825-35. PubMed PMID: 23944955.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - A multistep algorithm for processing and calibration of microdialysis continuous glucose monitoring data. AU - Mahmoudi,Zeinab, AU - Dencker Johansen,Mette, AU - Christiansen,Jens Sandahl, AU - Hejlesen,Ole Kristian, Y1 - 2013/08/14/ PY - 2013/8/16/entrez PY - 2013/8/16/pubmed PY - 2014/6/10/medline SP - 825 EP - 35 JF - Diabetes technology & therapeutics JO - Diabetes Technol Ther VL - 15 IS - 10 N2 - BACKGROUND: The deviation of continuous subcutaneous glucose monitoring (CGM) data from reference blood glucose measurements is substantial, and adequate signal processing is required to reduce the discrepancy between subcutaneous glucose and blood glucose values. The purpose of this study was to develop a multistep algorithm for the processing and calibration of continuous subcutaneous glucose monitoring data with high accuracy and short delay. Algorithm PRESENTATION: The algorithm comprises three steps: rate-limiting filtering, selective smoothing, and robust calibration. Initially, the algorithm detects nonphysiological glucose rate-of-change and corrects it with a weighted local polynomial. Noisy signal parts that require smoothing are then detected based on zero crossing count of the sensor signal first-order differences, and an exponentially weighted moving average smooths the noisy parts of the signal afterward. Finally, calibration is performed using a first-order polynomial as the conversion function, with coefficients being estimated using robust regression with a bi-square weight function. ALGORITHM PERFORMANCE: The performance of the algorithm was evaluated on 16 patients with type 1 diabetes mellitus. To compare the algorithm with state-of-the-art CGM data denoising and calibration, the rate-limiting filter and selective smoothing were replaced with an adaptive Kalman filter, and the calibration method was replaced with the calibration algorithm presented in one of the Medtronic (Northridge, CA) CGM patents. The median (mean) of the absolute relative deviation (ARD) of the sensor glucose values processed by the newly developed algorithm from capillary reference blood glucose measurements was 14.8% (22.6%), 10.6% (14.6%), and 8.9% (11.7%) in hypoglycemia, euglycemia, and hyperglycemia, respectively, whereas for the alternative algorithm, the median (mean) was 22.2% (26.9%), 12.1% (15.9%), and 8.8 (11.3%), respectively. The median (mean) ARD in all ranges was 10.3% (14.7%) for the new algorithm and 11.5% (15.8%) for the alternative algorithm. The new algorithm had an average delay of 2.1 min across the patients, and the alternative algorithm had an average delay of 2.9 min. CONCLUSIONS: The presented algorithm may increase the accuracy of CGM data. SN - 1557-8593 UR - https://www.unboundmedicine.com/medline/citation/23944955/A_multistep_algorithm_for_processing_and_calibration_of_microdialysis_continuous_glucose_monitoring_data_ L2 - https://www.liebertpub.com/doi/10.1089/dia.2013.0041?url_ver=Z39.88-2003&rfr_id=ori:rid:crossref.org&rfr_dat=cr_pub=pubmed DB - PRIME DP - Unbound Medicine ER -