AI of the needle: Here's how neural networks could detect nighttime low blood-sugar levels using your heart beat

Any one thought about actually testing this on diabetics? Er, no?


Academics have applied for a patent describing how a neural network can detect low blood-sugar levels by analyzing heartbeat patterns rather than a blood sample.

Keeping track of glucose levels is annoying and painful. Multiple times per day, diabetics have to prick their finger, place the small drop of blood on a test strip, and insert that strip into a glucometer to get a reading, and then dial up their insulin dosage, or eat or drink carbohydrates, as necessary. In the US, at least, these strips aren't cheap, thanks to the healthcare system.

The AI-based method developed by the team, however, is non-invasive. It only requires people to wear a device that can measure electrocardiograms (ECG), recordings of heartbeats made by sensors placed on the skin. Abnormal blood glucose levels can affect ECG readings; high levels of sugar lead to rapid heart rates, whereas low levels correlate to low heart rates.

The ECGs are then processed by a convolutional neural network (CNN) and a recurrent neural network (RNN) to flag up episodes of nocturnal hypoglycemia, a condition where glucose levels below a normal range during sleep.

Described as a “pilot study,” the researchers recruited four volunteers to wear devices that measure both ECGs and a non-invasive continuous glucose monitor (CGM). Over the course of up to 14 days, they studied each person’s pulse at times when their heart rates were normal and when they were affected by nocturnal hypoglycemic events. The data from the ECG and CGM were correlated and used to train the CNN and RNN to predict when blood glucose levels dip below normal levels from an individual’s heart rate.

Some of the ECG readings were held back for testing, and the results showed the team’s neural networks were on average accurate roughly 82 per cent time.

“Our approach enables personalized tuning of detection algorithms and emphasizes how hypoglycaemic events affect ECG in individuals,” said Leandro Pecchia, co-author of the paper and an associate professor of biomedical engineering at the University of Warwick, England. “Based on this information, clinicians can adapt the therapy to each individual.”

supercomputer

Why build your own cancer-sniffing neural network when this 1.3 exaflop supercomputer can do if for you?

READ MORE

But before any diabetics out there get their hopes up over such a device, the team admitted their patent has to go through much more clinical testing. Firstly, not only is their research sample size small, but none of the participants had type 1 or type 2 diabetes.

“Our study concerned the detection of nocturnal non-induced low glucose levels in healthy individuals; several clinical studies showed that cardiac changes could have different intensities in healthy, type 1 and type 2 diabetic persons,” the paper said.

So far, the results do show that applying deep learning on ECG can detect low blood glucose events and that training on personalized data makes it more effective for individuals. The goal is to eventually develop a device for diabetics that alerts them whenever their glucose levels dip to dangerous levels in their sleep.

The team applied to patent their technology “Electrocardiogra-based blood glucose level monitoring” in the UK in August last year, and described it in a paper published in Nature on Monday. ®

Broader topics


Other stories you might like

  • Experts: AI should be recognized as inventors in patent law
    Plus: Police release deepfake of murdered teen in cold case, and more

    In-brief Governments around the world should pass intellectual property laws that grant rights to AI systems, two academics at the University of New South Wales in Australia argued.

    Alexandra George, and Toby Walsh, professors of law and AI, respectively, believe failing to recognize machines as inventors could have long-lasting impacts on economies and societies. 

    "If courts and governments decide that AI-made inventions cannot be patented, the implications could be huge," they wrote in a comment article published in Nature. "Funders and businesses would be less incentivized to pursue useful research using AI inventors when a return on their investment could be limited. Society could miss out on the development of worthwhile and life-saving inventions."

    Continue reading
  • Declassified and released: More secret files on US govt's emergency doomsday powers
    Nuke incoming? Quick break out the plans for rationing, censorship, property seizures, and more

    More papers describing the orders and messages the US President can issue in the event of apocalyptic crises, such as a devastating nuclear attack, have been declassified and released for all to see.

    These government files are part of a larger collection of records that discuss the nature, reach, and use of secret Presidential Emergency Action Documents: these are executive orders, announcements, and statements to Congress that are all ready to sign and send out as soon as a doomsday scenario occurs. PEADs are supposed to give America's commander-in-chief immediate extraordinary powers to overcome extraordinary events.

    PEADs have never been declassified or revealed before. They remain hush-hush, and their exact details are not publicly known.

    Continue reading
  • Stolen university credentials up for sale by Russian crooks, FBI warns
    Forget dark-web souks, thousands of these are already being traded on public bazaars

    Russian crooks are selling network credentials and virtual private network access for a "multitude" of US universities and colleges on criminal marketplaces, according to the FBI.

    According to a warning issued on Thursday, these stolen credentials sell for thousands of dollars on both dark web and public internet forums, and could lead to subsequent cyberattacks against individual employees or the schools themselves.

    "The exposure of usernames and passwords can lead to brute force credential stuffing computer network attacks, whereby attackers attempt logins across various internet sites or exploit them for subsequent cyber attacks as criminal actors take advantage of users recycling the same credentials across multiple accounts, internet sites, and services," the Feds' alert [PDF] said.

    Continue reading

Biting the hand that feeds IT © 1998–2022