We use cookies to understand how you use our site and to improve your experience. This includes personalizing content and advertising. To learn more, click here. By continuing to use our site, you accept our use of cookies. Cookie Policy.

HospiMedica

Download Mobile App
Recent News AI Critical Care Surgical Techniques Patient Care Health IT Point of Care Business Focus

Artificial Intelligence Can Detect Glucose Levels via ECG

By HospiMedica International staff writers
Posted on 20 Jan 2020
Print article
Image: ECG heartbeat segments help identify hypoglycemia events (Photo courtesy of University of Warwick)
Image: ECG heartbeat segments help identify hypoglycemia events (Photo courtesy of University of Warwick)
A new study shows how artificial intelligence (AI) can be used to detect hypoglycemic events from raw electrocardiogram (ECG) signals.

Developed at the University of Warwick (Coventry, United Kingdom), the University of Napoli Federico II (Naples, Italy), Western University (WU; London, Canada), and other institutions, the personalized medicine approach uses AI to automatically detect nocturnal hypoglycemia with just a few heartbeats of raw ECG signal recorded with non-invasive, wearable devices. A visualization method then enables the clinicians to establish which part of the ECG signal is significantly associated with a hypoglycemic event in each individual subject.

The AI model is trained with each subject's own dataset, which is comprised of both ECG and glucose recordings as measured by two sensors worn for a period of 8-14 days. The researchers conducted two pilot studies involving eight healthy volunteers, which found that the average sensitivity and specificity of the AI approach for hypoglycemia detection was about 82%, comparable to current continuous glucose monitoring (CGM) device performance. The study was published on January 13, 2020, in Nature Scientific Reports.

“Fingerpicks are never pleasant, and in some circumstances particularly cumbersome. Our innovation consisted of using AI for automatically detecting hypoglycemia via few ECG beats. This is relevant because ECG can be detected in any circumstance, including sleeping,” said senior author Leandro Pecchia, PhD, of the University of Warwick School of Engineering. “Our approach enables personalized tuning of detection algorithms and emphasizes how hypoglycemic events affect ECG. Based on this information, clinicians can adapt the therapy to each individual.”

Hypoglycemia can cause pronounced physiological responses as a consequence of autonomic activation, principally of the sympatho-adrenal system, which results in the release of epinephrine (adrenaline). The autonomic stimulus provokes hemodynamic changes in order maintain a supply of glucose to the brain and promote the hepatic production of glucose. Hemodynamic changes associated with hypoglycemia include an increase in heart rate and peripheral systolic blood pressure, a fall in central blood pressure, reduced peripheral arterial resistance, and an increase in myocardial contractility, stroke volume, and cardiac output.

Related Links:
University of Warwick
University of Napoli Federico II
Western University


New
Gold Member
X-Ray QA Meter
T3 AD Pro
Flocked Fiber Swabs
Puritan® patented HydraFlock®
New
Pneumatic Stool
Avante 5-Leg Pneumatic Stool
New
Infant Phototherapy Unit
TRP100

Print article
Radcal

Channels

Critical Care

view channel
mage: The electroceutical epidermal patch is designed to inhibit bacterial growth (Photo courtesy of Saehyun Kim/University of Chicago)

Cutting-Edge Bioelectronic Device Offers Drug-Free Approach to Managing Bacterial Infections

Antibiotic-resistant infections pose an increasing threat to patient safety and healthcare systems worldwide. Recent estimates indicate that drug-resistant infections may rise by 70% by 2050, highlighting... Read more

Surgical Techniques

view channel
Image: Conceptual schematic showing microgrippers (µ-grippers) operating as biopsy tools in the upper urinary tract (Photo courtesy of Wangqu Liu, Yan Wan/Gracias Lab, Johns Hopkins University)

Microgrippers For Miniature Biopsies to Create New Cancer Diagnostic Screening Paradigm

The standard diagnosis of upper urinary tract cancers typically involves the removal of suspicious tissue using forceps, a procedure that is technically challenging and samples only a single region of the organ.... Read more

Patient Care

view channel
Image: The portable biosensor platform uses printed electrochemical sensors for the rapid, selective detection of Staphylococcus aureus (Photo courtesy of AIMPLAS)

Portable Biosensor Platform to Reduce Hospital-Acquired Infections

Approximately 4 million patients in the European Union acquire healthcare-associated infections (HAIs) or nosocomial infections each year, with around 37,000 deaths directly resulting from these infections,... Read more

Health IT

view channel
Image: First ever institution-specific model provides significant performance advantage over current population-derived models (Photo courtesy of Mount Sinai)

Machine Learning Model Improves Mortality Risk Prediction for Cardiac Surgery Patients

Machine learning algorithms have been deployed to create predictive models in various medical fields, with some demonstrating improved outcomes compared to their standard-of-care counterparts.... Read more

Point of Care

view channel
Image: The acoustic pipette uses sound waves to test for biomarkers in blood (Photo courtesy of Patrick Campbell/CU Boulder)

Handheld, Sound-Based Diagnostic System Delivers Bedside Blood Test Results in An Hour

Patients who go to a doctor for a blood test often have to contend with a needle and syringe, followed by a long wait—sometimes hours or even days—for lab results. Scientists have been working hard to... Read more