Artificial Intelligence (AI) can Detect Low-Glucose Levels via ECG without Fingerpick Test

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.

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 today, the 13th January 2020 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 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 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.

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 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.

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.

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.

Dr Leandro Pecchia comments: "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."

Mihaela Porumb, Saverio Stranges, Antonio Pescapè, Leandro Pecchia.
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.

Most Popular Now

IBM Watson Health Recognizes Top-Perform…

IBM (NYSE: IBM) Watson Health® announced its 2020 Fortune/IBM Watson Health 100 Top Hospitals list and 15 Top Health Systems award winners, naming the top-performing hospitals and health systems in...

Chatbots can Ease Medical Providers' Bur…

COVID-19 has placed tremendous pressure on health care systems, not only for critical care but also from an anxious public looking for answers. Research from the Indiana University Kelley School...

Abbott Receives FDA Approval for New Hea…

Abbott (NYSE: ABT) announced that the U.S. Food and Drug Administration (FDA) has approved the company's next-generation Gallant™ implantable cardioverter defibrillator (ICD) and cardiac resynchronization therapy defibrillator (CRT-D) devices. The...

The New Tattoo: Drawing Electronics on S…

One day, people could monitor their own health conditions by simply picking up a pencil and drawing a bioelectronic device on their skin. In a new study, University of Missouri...

Towards an AI Diagnosis Like the Doctor…

Artificial intelligence (AI) is an important innovation in diagnostics, because it can quickly learn to recognize abnormalities that a doctor would also label as a disease. But the way that...

SARS-CoV-2 Antibody Test from Siemens He…

Public Health England, in partnership with the University of Oxford, recently conducted a head-to-head evaluation of four commercial immunoassay tests available in the UK and used for the detection of...

Researchers Develop Software to Find Dru…

Washington State University researchers have developed an easy-to-use software program to identify drug-resistant genes in bacteria. The program could make it easier to identify the deadly antimicrobial resistant bacteria that...

Philips Introduces First-of-a-Kind Mobil…

Royal Philips (NYSE: PHG, AEX: PHIA), a global leader in health technology, today announced it introduced first-of-its-kind mobile Intensive Care Units (ICUs) in India. Designed to meet the critical-care requirements...

Proposed Framework for Integrating Chatb…

While the technology for developing artificial intelligence-powered chatbots has existed for some time, a new viewpoint piece in JAMA lays out the clinical, ethical, and legal aspects that must be...

Clinical-Grade Wearables Offer Continuou…

Although it might be tempting to rely on your fitness tracker to catch early signs of COVID-19, Northwestern University researchers caution that consumer wearables are not sophisticated enough to monitor...

World's Smallest Imaging Device has Hear…

A team of researchers led by the University of Adelaide and University of Stuttgart has used 3D micro-printing to develop the world's smallest, flexible scope for looking inside blood vessels...

Optimizing Neural Networks on a Brain-In…

Many computational properties are maximized when the dynamics of a network are at a "critical point", a state where systems can quickly change their overall characteristics in fundamental ways, transitioning...