A multiparameter model for non-invasive detection of hypoglycemia
Elvebakk, Ole; Tronstad, Christian; Birkeland, Kåre I.; Jenssen, Trond Geir; Bjørgaas, Marit Ragnhild Rokne; Gulseth, Hanne Løvdal; Kalvøy, Håvard; Høgetveit, Jan Olav; Martinsen, Ørjan Grøttem
Journal article, Peer reviewed
Accepted version
Åpne
Permanent lenke
http://hdl.handle.net/11250/2638014Utgivelsesdato
2019Metadata
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- Publikasjoner fra CRIStin - NTNU [38289]
- St. Olavs hospital [2536]
Sammendrag
Objective: Severe hypoglycemia is the most serious acute complication for people with type 1 diabetes (T1D). Approximately 25% of people with T1D have impaired ability to recognize impending hypoglycemia, and nocturnal episodes are feared. Approach: We have investigated the use of non-invasive sensors for detection of hypoglycemia based on a mathematical model which combines several sensor measurements to identify physiological responses to hypoglycemia. Data from randomized single-blinded euglycemic and hypoglycemic glucose clamps in 20 participants with T1D and impaired awareness of hypoglycemia was used in the analyses. Main results: Using a sensor combination of sudomotor activity at three skin sites, ECG-derived heart rate and heart rate corrected QT interval, near-infrared and bioimpedance spectroscopy; physiological responses associated with hypoglycemia could be identified with an F1 score accuracy up to 88%. Significance: We present a novel model for identification of non-invasively measurable physiological responses related to hypoglycemia, showing potential for detection of moderate hypoglycemia using a wearable sensor system