Researchers develop AI to detect blood sugar levels from ECG

In order to analyse blood sugar levels, patients must encounter an uncomfortable fingerprick test and as a result of this, researchers at the University of Warwick have developed an artificial intelligence (AI) system to analyse data from a wearable electrocardiograph (ECG), to detect hypoglycaemic events.

The researchers published their findings in the journal ‘ Scientific Reports ‘ which revealed that the AI was able to detect hypoglycaemic events with an accuracy of eighty-two percent which is equivalent to the current continuous glucose monitors (CGM).

A member from Warwick’s School of Engineering, Dr. Leandro Pecchia, stated: “Fingerpicks are never pleasant and in some circumstances are particularly cumbersome. Taking fingerpicks during the night certainly is unpleasant, especially for patients in paediatric 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. The differences highlighted above could explain why previous studies using ECG to detect hypoglycaemic events failed. The performance of AI algorithms trained over cohort ECG data would be hindered by these inter subject differences. Our approach enables personalised tuning of detection algorithms and emphasises 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.”