Currently continuous glucose monitors (CGM) are available by the NHS for hypoglycemia detection (sugar levels into blood or derma). They measure glucose in interstitial fluid using an invasive sensor with a little needle, which sends alarms and data to a display device. In many cases, they require calibration twice a day with invasive finger-prick blood glucose level tests.

However, Dr Leandro Pecchia's team at the University of Warwick published results in a paper titled 'Precision Medicine and Artificial Intelligence: A Pilot Study on Deep Learning for Hypoglycemic Events Detection based on ECG' in the Nature Springer journal Scientific Reports proving that using the latest findings of artificial intelligence (i.e., deep learning), they can detect hypoglycemic events from raw ECG signals acquired with off-the-shelf non-invasive wearable sensors.

Two pilot studies with healthy volunteers found the average sensitivity and specificity approximately 82% for hypoglycemia detection, which is comparable with the current CGM performance, although non-invasive.

Dr. Leandro Pecchia from the School of Engineering at the University of Warwick comments:

"Fingerpicks are never pleasant and in some circumstances are particularly cumbersome. Taking fingerpick during the night certainly is unpleasant, especially for patients in pediatric age.

"Our innovation consisted in using artificial intelligence for automatic detecting hypoglycemia via few ECG beats. This is relevant because ECG can be detected in any circumstance, including sleeping."

This result is possible because the Warwick AI model is trained with each subject's own data. Intersubjective differences are so significant, that training the system using cohort data would not give the same results. Likewise, personalized therapy based on our system could be more effective than current approaches.

Dr Leandro Pecchia comments:

"The differences highlighted above could explain why previous studies using ECG to detect hypoglycemic events failed. The performance of AI algorithms trained over cohort ECG-data would be hindered by these inter-subject differences."

"Our approach enable personalized tuning of detection algorithms and emphasise how hypoglycaemic events affect ECG in individuals. Basing on this information, clinicians can adapt the therapy to each individual. Clearly more clinical research is required to confirm these results in wider populations. This is why we are looking for partners."