United Kingdom: Artificial Intelligence AI Detects Low-Glucose Levels and Makes Fingerpicks Superfluous

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.

Warwick/United Kingdom — 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 technology for detecting low glucose levels via ECG using a non-invasive wearable sensor, which with the latest Artificial Intelligence can detect hypoglycaemic events from raw ECG signals has been made by researchers from the University of Warwick. 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.

The 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 4 mmol/L glucose value, which is considered the significant threshold for hypoglycaemic events. The grey area surrounding the continuous line reflects the measurement error bar.

The Warwick model highlights how the ECG changes in each subject during a hypoglycaemic event. The figure below is an exemplar. The solid lines represent the average heartbeats for two different subjects when the glucose level is normal (green line) or low (red line). The red and green shadows represent the standard deviation of the heartbeats around the mean. A comparison highlights that these two subjects have different ECG waveform changes during hypo events. In particular, Subject 1 presents a visibly longer QT interval during hypo, while the subject 2 does not.

ECG Changes Between two Subjects

The vertical bars represent the relative importance of each ECG wave in determining if a heartbeat is classified as hypo or normal.

From these bars, a trained clinician sees that for Subject 1, the T-wave displacement influences classification, reflecting that when the subject is in hypo, the repolarisation of the ventricles is slower.

In Subject 2, the most important components of the ECG are the P-wave and the rising of the T-wave, suggesting that when this subject is in hypo, the depolarisation of the atria and the threshold for ventricular activation are particularly affected. This could influence subsequent clinical interventions.

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, personalised therapy based on our system could be more effective than current approaches.

Original Article: Porumb, M., Stranges, S., Pescapè, A. et al. Precision Medicine and Artificial Intelligence: A Pilot Study on Deep Learning for Hypoglycemic Events Detection based on ECG. Sci Rep 10, 170 (2020) doi:10.1038/s41598-019-56927-5

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