A new technique developed by researchers at the University of Warwick uses the latest findings of Artificial Intelligence to detect hypoglycaemic events from raw ECG signals, via wearable sensors and without requiring fingerprick tests.
Fingerpicks can often be painful and deterring patient compliance. The new technology works with an 82 percent reliability, and could replace the need for invasive finger-prick testing with a needle, which could be particularly useful for paediatric age patients.
Tracking sugar in the blood is crucial for both healthy individuals and diabetic patients. Current methods to measure glucose requires needles and repeated fingerpicks over the day. Fingerpicks can often be painful, deterring patient compliance.
Currently Continuous Glucose Monitors (CGM) are available by the NHS for hypoglycaemia 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 have 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 journal Scientific Reports proving that using the latest findings of Artificial Intelligence (i.e., deep learning), they can detect hypoglycaemic 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 percent for hypoglycaemia 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 paediatric age. Our innovation consisted in using artificial intelligence for automatic detecting hypoglycaemia via few ECG beats. This is relevant because ECG can be detected in any circumstance, including sleeping."
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, personalised therapy could be more effective than current approaches.
Dr Leandro Pecchia comments: "Our approach enable personalised 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."