Physiological measurement · 2019
A multiparameter model for non-invasive detection of hypoglycemia.
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Elvebakk Ole, Tronstad Christian, Birkeland Kåre I, Jenssen Trond G, Bjørgaas Marit R, Gulseth Hanne L, Kalvøy Håvard, Høgetveit Jan O, Martinsen Ørjan G
Аннотация
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.
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.