AI can detect low-glucose levels via ECG without fingerpick test

AI can detect low-glucose levels via ECG without fingerpick test

January 15, 2020 0 By Jose Scott


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. 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. The technology works with an 82% reliability,
and could replace the need for invasive finger-prick testing with a needle, which could be particularly
useful for paediatric age patients. 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. The research team has published a paper in
the journal Nature’s 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% for hypoglycaemia detection,
which is comparable with the current CGM performance, although non-invasive. 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. This 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 figure shows the output of the algorithms
over the time: the green line represents normal glucose levels, while the red line represents
the low glucose levels. The horizontal line represents the 4mmol/L
glucose value, which is considered the significant threshold for hypoglycaemic events. The grey area surrounding the continuous line
reflects the measurement error bar.