New AI Platform Can Detect Low Glucose Levels Via ECG Without The Need For A Blood Tests
Source: Thailand Medical News Jan 19, 2020 4 years, 10 months, 1 day, 21 hours, 10 minutes ago
A novel 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.
At the moment, 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.
Dr. Leandro Pecchia's team at the University of Warwick published results in a paper titled "Precision Medicine and
artificial intelligence (AI): 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
hypoglycaemic events from raw
ECG signals acquired with off-the-shelf non-invasive wearable sensors.
Two main 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
"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." said Dr. Leandro Pecchia from the School of Engineering at the University of Warwick during a phone interview with
Thailand Medical News.
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The diagram 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.
The novel 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.
In the diagram, 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 the diagram of 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 study 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.
"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 enable personalised tuning of detection algorithms and emphasize 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." said Dr. Leandro Pecchia.
Reference: Mihaela Porumb et al. Precision Medicine and Artificial Intelligence: A Pilot Study on Deep Learning for Hypoglycemic Events Detection based on ECG, Scientific Reports (2020). DOI: 10.1038/s41598-019-56927-5
