Fashionable earbuds can be used to detect an irregular heartbeat, by tracking the electrical activity of the heart. Researchers at Imperial College London have created a device worn inside the ear, and just a little larger than an earbud, which can take an ECG reading all day long. A new study has found it performs about as well as a conventional ECG using electrodes on the chest, for two out of three measurements. But while the earbud detects a weaker signal from the heart than a chest ECG, because it is further away across the body, it has the advantage of being worn easily for hours at a time. That could help people detect much more quickly, without visiting a doctor, if they have a heart rhythm disorder.

Artificial intelligence can examine electrocardiogram (ECG) test results, a common medical test, to pinpoint patients at higher risk of developing a potentially dangerous irregular heartbeat (arrhythmia) or of dying within the next year, according to two preliminary studies to be presented at the American Heart Association's Scientific Sessions 2019--November 16-18 in Philadelphia. Researchers used more than 2 million ECG results from more than three decades of archived medical records in Pennsylvania/New Jersey's Geisinger Health System to train deep neural networks--advanced, multi-layered computational structures. Both studies, from the same group of researchers, are among the first to use artificial intelligence to predict future events from an ECG rather than to detect current health problems, the scientists noted. "This is exciting and provides more evidence that we are on the verge of a revolution in medicine where computers will be working alongside physicians to improve patient care," said Brandon Fornwalt, M.D., Ph.D., senior author on both studies and associate professor and chair of the Department of Imaging Science and Innovation at Geisinger in Danville, Pennsylvania. Researchers speculated that a deep learning model could predict irregular heart rhythms, known as atrial fibrillation (AF), before it develops.

Artificial intelligence technology based on a deep learning model could help cardiologists predict irregular heart rhythms, known as atrial fibrillation, before it develops. WHY IT MATTERS That's the conclusion drawn from two studies to be presented at the American Heart Association Scientific Sessions 2019 and conducted by Geisinger researchers. A team of scientists trained a neural network to evaluate electrocardiograms to predict which patients were likely to develop an irregular heartbeat, using the AI model to analyze the results of 1.77 million ECGs and other records from almost 400,000 patients. Researchers trained deep neural networks using ECG results from across 30 years of archived medical records in Pennsylvania and New Jersey's Geisinger Health System, finding the AI was able to provide longer-term prognostication and more accurately identify at-risk patients. The model was also able to predict which patients would develop an irregular heartbeat, even when doctors interpreted the test results as normal, by analyzing 15 segments of data comprised of more than 30,000 data points for each ECG.

The company, Cambridge Heartwear, hopes to use its wireless monitor to improve the detection of irregular and dangerous heart rhythms and reduce the impact of stroke and stroke-related mortality and morbidity, which affects 120,000 people in the UK each year. Professor Roberto Cipolla from Cambridge's Department of Engineering met cardiologist and clinical academic Dr Rameen Shakur in 2015, a year after Roberto's father had died of a stroke. Their ongoing research collaboration has now led to the formation of Cambridge Heartwear, a company based on the Cambridge Science Park. The company's device, called Heartsense, includes a multiple lead ECG, oxygen sensing, temperature and tracking device which can be comfortably worn by patients for early screening. Sensors are enclosed in a robust waterproof casing, and the data produced is far more sensitive than that from current single lead wearable devices, as the development team have used their knowledge of clinical anatomy and electrophysiology to place leads for maximal signal output.

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Posted on January 15th, 2019 by Dr. Francis Collins Thanks to advances in wearable health technologies, it's now possible for people to monitor their heart rhythms at home for days, weeks, or even months via wireless electrocardiogram (EKG) patches. In fact, my Apple Watch makes it possible to record a real-time EKG whenever I want. For true medical benefit, however, the challenge lies in analyzing the vast amounts of data--often hundreds of hours worth per person--to distinguish reliably between harmless rhythm irregularities and potentially life-threatening problems. Now, NIH-funded researchers have found that artificial intelligence (AI) can help. A powerful computer "studied" more than 90,000 EKG recordings, from which it "learned" to recognize patterns, form rules, and apply them accurately to future EKG readings.

The Apple Watch has been found to detect a heart condition that affects some 2.7 million people in the US, a new study has revealed. By pairing the smartwatch's heart rate sensors with artificial intelligence, researchers developed an algorithm capable of distinguishing an irregular heartbeat, known as atrial fibrillation, from a normal heart rhythm - and with 97 percent accuracy. Atrial fibrillation, although easily treatable, has been difficult to diagnose and the team believes their work could pave the way for new methods to identify the abnormality. The Apple Watch has been found to detect a heart condition that affects some 2.7 million people in the US, a new study has revealed. The algorithm was accurate 97 percent of the time using the smartwatch's heart rate sensor (stock) University of California, San Francisco, in collaboration with the app Cardiogram, trained a deep neural network with heart readings from 6,158 Cardiogram users.