Most patients with diabetes still rely on finger-prick blood tests to measure their glucose levels. Some use wearable continuous glucose monitors, but even those typically require twice daily calibrations against the finger-prick method. It has been known for a while that an electrocardiogram (ECG) can show signs of abnormal glucose levels, but the reliability of this method has been hampered by the individual responses that different patients exhibit in their ECG waveforms.

Now, researchers at the University of Warwick in the UK have shown that they can detect dangerous hypoglycemic events with an accuracy of 82% by simply analyzing ECG graphs generated by a commercially available wearable device (Medtronic Zephyr BioPatch HP). This is comparable to the current capabilities of invasive continuous glucometers. If these results are confirmed in more extensive studies, the technology may help with pediatric patients, aid in preventing hypoglycemia during sleep, and lead to a major improvement in the quality of life for many patients with diabetes.

The new system relies on a deep learning approach that can identify how the heartbeats of different people are affected by hypoglycemia. The ECG signals of each patient are recorded and hypoglycemic events noted using traditional means. Given enough data, the Warwick system notices the ECG signal biomarkers that come up in cases of hypoglycemia, which it can spot during subsequent analysis. Making things easier for clinicians is an annotative approach that makes sure that the aspects of the ECG that point to hypoglycemia are highlighted.

Reference: https://www.medgadget.com/2020/01/ai-powers-personalized-medicine-approach-to-detecting-hypoglycemia.html