Machine learning for non-invasive sensing of hypoglycaemia while driving in people with diabetes
Journal
Diabetes, Obesity and Metabolism
Type
journal article
Date Issued
2023-02-15
Author(s)
Lehmann, Vera
;
Zueger, Thomas
;
Maritsch, Martin
;
Kraus, Mathias
;
Albrecht, Caroline
;
Bérubé, Caterina
;
Feuerriegel, Stefan
;
;
;
Styger, Naïma
;
Lagger, Sophie
;
Laimer, Markus
;
;
Stettler, Christoph
Abstract (De)
Aim
To develop and evaluate the concept of a non-invasive machine learning (ML) approach for detecting hypoglycaemia based exclusively on combined driving (CAN) and eye tracking (ET) data.
Materials and Methods
We first developed and tested our ML approach in pronounced hypoglycaemia, and then we applied it to mild hypoglycaemia to evaluate its early warning potential. For this, we conducted two consecutive, interventional studies in individuals with type 1 diabetes. In study 1 (n = 18), we collected CAN and ET data in a driving simulator during euglycaemia and pronounced hypoglycaemia (blood glucose [BG] 2.0-2.5 mmol L−1). In study 2 (n = 9), we collected CAN and ET data in the same simulator but in euglycaemia and mild hypoglycaemia (BG 3.0-3.5 mmol L−1).
Results
Here, we show that our ML approach detects pronounced and mild hypoglycaemia with high accuracy (area under the receiver operating characteristics curve 0.88 ± 0.10 and 0.83 ± 0.11, respectively).
Conclusions
Our findings suggest that an ML approach based on CAN and ET data, exclusively, enables detection of hypoglycaemia while driving. This provides a promising concept for alternative and non-invasive detection of hypoglycaemia.
To develop and evaluate the concept of a non-invasive machine learning (ML) approach for detecting hypoglycaemia based exclusively on combined driving (CAN) and eye tracking (ET) data.
Materials and Methods
We first developed and tested our ML approach in pronounced hypoglycaemia, and then we applied it to mild hypoglycaemia to evaluate its early warning potential. For this, we conducted two consecutive, interventional studies in individuals with type 1 diabetes. In study 1 (n = 18), we collected CAN and ET data in a driving simulator during euglycaemia and pronounced hypoglycaemia (blood glucose [BG] 2.0-2.5 mmol L−1). In study 2 (n = 9), we collected CAN and ET data in the same simulator but in euglycaemia and mild hypoglycaemia (BG 3.0-3.5 mmol L−1).
Results
Here, we show that our ML approach detects pronounced and mild hypoglycaemia with high accuracy (area under the receiver operating characteristics curve 0.88 ± 0.10 and 0.83 ± 0.11, respectively).
Conclusions
Our findings suggest that an ML approach based on CAN and ET data, exclusively, enables detection of hypoglycaemia while driving. This provides a promising concept for alternative and non-invasive detection of hypoglycaemia.
Language
English
HSG Classification
contribution to scientific community
Refereed
Yes
Publisher
Wiley Online Library
Publisher place
Wiley Online Library
Volume
26
Subject(s)
Eprints ID
269532
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Diabetes Obesity Metabolism - 2023 - Lehmann - Machine learning for non‐invasive sensing of hypoglycaemia while driving in.pdf
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