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Using Artificial Intelligence to Catch Irregular Heartbeats

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ECG Readout
Credit: gettyimages/enot-poloskun

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. (I’m glad to say I am in normal sinus rhythm.)

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 computer became so “smart” that it could classify 10 different types of irregular heart rhythms, including atrial fibrillation (AFib). In fact, after just seven months of training, the computer-devised algorithm was as good—and in some cases even better than—cardiology experts at making the correct diagnostic call.

EKG tests measure electrical impulses in the heart, which signal the heart muscle to contract and pump blood to the rest of the body. The precise, wave-like features of the electrical impulses allow doctors to determine whether a person’s heart is beating normally.

For example, in people with AFib, the heart’s upper chambers (the atria) contract rapidly and unpredictably, causing the ventricles (the main heart muscle) to contract irregularly rather than in a steady rhythm. This is an important arrhythmia to detect, even if it may only be present occasionally over many days of monitoring. That’s not always easy to do with current methods.

Here’s where the team, led by computer scientists Awni Hannun and Andrew Ng, Stanford University, Palo Alto, CA, saw an AI opportunity. As published in Nature Medicine, the Stanford team started by assembling a large EKG dataset from more than 53,000 people [1]. The data included various forms of arrhythmia and normal heart rhythms from people who had worn the FDA-approved Zio patch for about two weeks.

The Zio patch is a 2-by-5-inch adhesive patch, worn much like a bandage, on the upper left side of the chest. It’s water resistant and can be kept on around the clock while a person sleeps, exercises, or takes a shower. The wireless patch continuously monitors heart rhythms, storing EKG data for later analysis.

The Stanford researchers looked to machine learning to process all the EKG data. In machine learning, computers rely on large datasets of examples in order to learn how to perform a given task. The accuracy improves as the machine “sees” more data.

But the team’s real interest was in utilizing a special class of machine learning called deep neural networks, or deep learning. Deep learning is inspired by how our own brain’s neural networks process information, learning to focus on some details but not others.

In deep learning, computers look for patterns in data. As they begin to “see” complex relationships, some connections in the network are strengthened while others are weakened. The network is typically composed of multiple information-processing layers, which operate on the data and compute increasingly complex and abstract representations.

Those data reach the final output layer, which acts as a classifier, assigning each bit of data to a particular category or, in the case of the EKG readings, a diagnosis. In this way, computers can learn to analyze and sort highly complex data using both more obvious and hidden features.

Ultimately, the computer in the new study could differentiate between EKG readings representing 10 different arrhythmias as well as a normal heart rhythm. It could also tell the difference between irregular heart rhythms and background “noise” caused by interference of one kind or another, such as a jostled or disconnected Zio patch.

For validation, the computer attempted to assign a diagnosis to the EKG readings of 328 additional patients. Independently, several expert cardiologists also read those EKGs and reached a consensus diagnosis for each patient. In almost all cases, the computer’s diagnosis agreed with the consensus of the cardiologists. The computer also made its calls much faster.

Next, the researchers compared the computer’s diagnoses to those of six individual cardiologists who weren’t part of the original consensus committee. And, the results show that the computer actually outperformed these experienced cardiologists!

The findings suggest that artificial intelligence can be used to improve the accuracy and efficiency of EKG readings. In fact, Hannun reports that iRhythm Technologies, maker of the Zio patch, has already incorporated the algorithm into the interpretation now being used to analyze data from real patients.

As impressive as this is, we are surely just at the beginning of AI applications to health and health care. In recognition of the opportunities ahead, NIH has recently launched a working group on AI to explore ways to make the best use of existing data, and harness the potential of artificial intelligence and machine learning to advance biomedical research and the practice of medicine.

Meanwhile, more and more impressive NIH-supported research featuring AI is being published. In my next blog, I’ll highlight a recent paper that uses AI to make a real difference for cervical cancer, particularly in low resource settings.

Reference:

[1] Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network. Hannun AY, Rajpurkar P, Haghpanahi M, Tison GH, Bourn C, Turakhia MP, Ng AY.
Nat Med. 2019 Jan;25(1):65-69.

Links:

Arrhythmia (National Heart, Lung, and Blood Institute/NIH)

Video: Artificial Intelligence: Collecting Data to Maximize Potential (NIH)

Andrew Ng (Palo Alto, CA)

NIH Support: National Heart, Lung, and Blood Institute


Wearable mHealth Device Detects Abnormal Heart Rhythms Earlier

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Zio patch

Caption: Woman wearing a Zio patch
Credit: Adapted from JAMA Network Summary Video

As many as 6 million Americans experience a common type of irregular heartbeat, called atrial fibrillation (AFib), that can greatly increase their risk of stroke and heart failure [1]. There are several things that can be done to lower that risk, but the problem is that a lot of folks have no clue that their heart’s rhythm is out of whack!

So, what can we do to detect AFib and get people into treatment before it’s too late? New results from an NIH-funded study lend additional support to the idea that one answer may lie in wearable health technology: a wireless electrocardiogram (EKG) patch that can be used to monitor a person’s heart rate at home.


All of Us Needs All of You

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I’ve got some exciting news to share with you: as of May 6, 2018, NIH’s All of Us Research Program is open to everyone living in the United States, age 18 and older. That means that you, along with your family and friends, can join with 1 million or more Americans from all walks of life to create an unprecedented research resource that will speed biomedical breakthroughs and transform medicine.

To launch this historic undertaking, All of Us yesterday held community events at seven sites across the nation, from Alabama to Washington state. I took part in an inspiring gathering at the Abyssinian Baptist Church in New York’s Harlem neighborhood, where I listened to community members talk about how important it is for everyone to be able to take part in this research. I shared information on how All of Us will help researchers devise new ways of improving the health of everyone in this great nation.


Wearable Scanner Tracks Brain Activity While Body Moves

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Credit: Wellcome Centre for Human Neuroimaging, University College London.

In recent years, researchers fueled by the BRAIN Initiative and many other NIH-supported efforts have made remarkable progress in mapping the human brain in all its amazing complexity. Now, a powerful new imaging technology promises to further transform our understanding [1]. This wearable scanner, for the first time, enables researchers to track neural activity in people in real-time as they do ordinary things—be it drinking tea, typing on a keyboard, talking to a friend, or even playing paddle ball.

This new so-called magnetoencephalography (MEG) brain scanner, which looks like a futuristic cross between a helmet and a hockey mask, is equipped with specialized “quantum” sensors. When placed directly on the scalp surface, these new MEG scanners can detect weak magnetic fields generated by electrical activity in the brain. While current brain scanners weigh in at nearly 1,000 pounds and require people to come to a special facility and remain absolutely still, the new system weighs less than 2 pounds and is capable of generating 3D images even when a person is making motions.


What a Year It Was! A Look Back at Research Progress in 2017

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I want to wish everyone a Happy New Year! Hope your 2018 is off to a great start.

Over the holidays, the journal Science published its annual, end-of-the-year list of research breakthroughs, from anthropology to zoology. I always look forward to seeing the list and reflecting on some of the stunning advances reported in the past 12 months. Last year was no exception. Science’s 2017 Breakthrough of the Year, as chosen by its editors, was in the field of astrophysics. Scientists were able to witness the effects of the collision of two neutron stars—large stars with collapsed inner cores—smacking into each other 130 million light years away. How cool is that!

Numbered prominently among the nine other breakthroughs were five from biomedicine: gene therapy, gene editing, cancer immunotherapy, cryo-EM, and biology preprints. All involved varying degrees of NIH support, and all drew great interest from readers. In fact, three of the top four vote-getters in the “People’s Choice” category came from biomedicine. That includes the People’s 2017 Breakthrough of the Year: gene therapy success. And so, in what has become a Director’s Blog tradition, I’ll kick off our new year of posts by taking a closer look at these biomedical breakthroughs—starting with the little girl in the collage above, and moving clockwise around the images:


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