Retrospective Continuous-Time Blood Glucose Estimation in Free Living Conditions with a Non-Invasive Multisensor Device
Even if still at an early stage of development, non-invasive continuous glucose monitoring (NI-CGM) sensors represent a promising technology for optimizing diabetes therapy. Recent studies showed that the Multisensor provides useful information about glucose dynamics with a mean absolute relative di...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2019-08-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/19/17/3677 |
| _version_ | 1798025924982079488 |
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| author | Giada Acciaroli Mattia Zanon Andrea Facchinetti Andreas Caduff Giovanni Sparacino |
| author_facet | Giada Acciaroli Mattia Zanon Andrea Facchinetti Andreas Caduff Giovanni Sparacino |
| author_sort | Giada Acciaroli |
| collection | DOAJ |
| description | Even if still at an early stage of development, non-invasive continuous glucose monitoring (NI-CGM) sensors represent a promising technology for optimizing diabetes therapy. Recent studies showed that the Multisensor provides useful information about glucose dynamics with a mean absolute relative difference (MARD) of 35.4% in a fully prospective setting. Here we propose a method that, exploiting the same Multisensor measurements, but in a retrospective setting, achieves a much better accuracy. Data acquired by the Multisensor during a long-term study are retrospectively processed following a two-step procedure. First, the raw data are transformed to a blood glucose (BG) estimate by a multiple linear regression model. Then, an enhancing module is applied in cascade to the regression model to improve the accuracy of the glucose estimation by retrofitting available BG references through a time-varying linear model. MARD between the retrospectively reconstructed BG time-series and reference values is 20%. Here, 94% of values fall in zone A or B of the Clarke Error Grid. The proposed algorithm achieved a level of accuracy that could make this device a potential complementary tool for diabetes management and also for guiding prediabetic or nondiabetic users through life-style changes. |
| first_indexed | 2024-04-11T18:26:47Z |
| format | Article |
| id | doaj.art-b77c57ebeed743f6af96af6d791a8475 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-11T18:26:47Z |
| publishDate | 2019-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-b77c57ebeed743f6af96af6d791a84752022-12-22T04:09:36ZengMDPI AGSensors1424-82202019-08-011917367710.3390/s19173677s19173677Retrospective Continuous-Time Blood Glucose Estimation in Free Living Conditions with a Non-Invasive Multisensor DeviceGiada Acciaroli0Mattia Zanon1Andrea Facchinetti2Andreas Caduff3Giovanni Sparacino4Department of Information Engineering, University of Padova, 35131 Padova, ItalyBiovotion AG, 8008 Zurich, SwitzerlandDepartment of Information Engineering, University of Padova, 35131 Padova, ItalyBiovotion AG, 8008 Zurich, SwitzerlandDepartment of Information Engineering, University of Padova, 35131 Padova, ItalyEven if still at an early stage of development, non-invasive continuous glucose monitoring (NI-CGM) sensors represent a promising technology for optimizing diabetes therapy. Recent studies showed that the Multisensor provides useful information about glucose dynamics with a mean absolute relative difference (MARD) of 35.4% in a fully prospective setting. Here we propose a method that, exploiting the same Multisensor measurements, but in a retrospective setting, achieves a much better accuracy. Data acquired by the Multisensor during a long-term study are retrospectively processed following a two-step procedure. First, the raw data are transformed to a blood glucose (BG) estimate by a multiple linear regression model. Then, an enhancing module is applied in cascade to the regression model to improve the accuracy of the glucose estimation by retrofitting available BG references through a time-varying linear model. MARD between the retrospectively reconstructed BG time-series and reference values is 20%. Here, 94% of values fall in zone A or B of the Clarke Error Grid. The proposed algorithm achieved a level of accuracy that could make this device a potential complementary tool for diabetes management and also for guiding prediabetic or nondiabetic users through life-style changes.https://www.mdpi.com/1424-8220/19/17/3677diabetescontinuous glucose monitoringnon-invasivemultisensor |
| spellingShingle | Giada Acciaroli Mattia Zanon Andrea Facchinetti Andreas Caduff Giovanni Sparacino Retrospective Continuous-Time Blood Glucose Estimation in Free Living Conditions with a Non-Invasive Multisensor Device Sensors diabetes continuous glucose monitoring non-invasive multisensor |
| title | Retrospective Continuous-Time Blood Glucose Estimation in Free Living Conditions with a Non-Invasive Multisensor Device |
| title_full | Retrospective Continuous-Time Blood Glucose Estimation in Free Living Conditions with a Non-Invasive Multisensor Device |
| title_fullStr | Retrospective Continuous-Time Blood Glucose Estimation in Free Living Conditions with a Non-Invasive Multisensor Device |
| title_full_unstemmed | Retrospective Continuous-Time Blood Glucose Estimation in Free Living Conditions with a Non-Invasive Multisensor Device |
| title_short | Retrospective Continuous-Time Blood Glucose Estimation in Free Living Conditions with a Non-Invasive Multisensor Device |
| title_sort | retrospective continuous time blood glucose estimation in free living conditions with a non invasive multisensor device |
| topic | diabetes continuous glucose monitoring non-invasive multisensor |
| url | https://www.mdpi.com/1424-8220/19/17/3677 |
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