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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


Modeling Hypertrophic Cardiomyopathy in a Dish

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Image of cardiac fibers

Credit: Zhen Ma, University of California, Berkeley

Researchers have learned in recent years how to grow miniature human hearts in a dish. These “organoids” beat like the real thing and have allowed researchers to model many key aspects of how the heart works. What’s been really tough to model in a dish is how stresses on hearts that are genetically abnormal, such as in inherited familial cardiomyopathies, put people at greater risk for cardiac problems.

Enter the lab-grown human cardiac tissue pictured above. This healthy tissue comprised of the heart’s muscle cells, or cardiomyocytes (green, nuclei in red), was derived from induced pluripotent stem (iPS) cells. These cells are derived from adult skin or blood cells that are genetically reprogrammed to have the potential to develop into many different types of cells, including cardiomyocytes.


Gene Editing in Dogs Boosts Hope for Kids with Muscular Dystrophy

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Dystrophin before and after treatment

Caption: A CRISPR/cas9 gene editing-based treatment restored production of dystrophin proteins (green) in the diaphragm muscles of dogs with Duchenne muscular dystrophy.
Credit: UT Southwestern

CRISPR and other gene editing tools hold great promise for curing a wide range of devastating conditions caused by misspellings in DNA. Among the many looking to gene editing with hope are kids with Duchenne muscular dystrophy (DMD), an uncommon and tragically fatal genetic disease in which their muscles—including skeletal muscles, the heart, and the main muscle used for breathing—gradually become too weak to function. Such hopes were recently buoyed by a new study that showed infusion of the CRISPR/Cas9 gene editing system could halt disease progression in a dog model of DMD.

As seen in the micrographs above, NIH-funded researchers were able to use the CRISPR/Cas9 editing system to restore production of a critical protein, called dystrophin, by up to 92 percent in the muscle tissue of affected dogs. While more study is needed before clinical trials could begin in humans, this is very exciting news, especially when one considers that boosting dystrophin levels by as little as 15 percent may be enough to provide significant benefit for kids with DMD.


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.


Can Barbers Help Black Men Lower Their Blood Pressure?

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Barbershop trial

Caption: Barber Eric Muhammad (left) in his barbershop taking the blood pressure of patron.
Credit: Smidt Heart Institute, Cedars-Sinai Medical Center

You expect to have your blood pressure checked and treated when you visit the doctor’s office or urgent care clinic. But what about the barbershop? New research shows that besides delivering the customary shave and a haircut, barbers might be able to play a significant role in helping control high blood pressure.

High blood pressure, or hypertension, is a particularly serious health problem among non-Hispanic black men. So, in a study involving 52 black-owned barbershops in the Los Angeles area, barbers encouraged their regular, black male patrons, ages 35 to 79, to get their blood pressure checked at their shops [1]. Nearly 320 men turned out to have uncontrolled hypertension and enrolled in the study. In a randomized manner, barbers then encouraged these men to do one of two things: attend one-on-one barbershop meetings with pharmacists who could prescribe blood pressure medicines, or set up appointments with their own doctors and consider making lifestyle changes.

The result? More than 63 percent of the men who received medications prescribed by specially-trained pharmacists lowered their blood pressure to healthy levels within 6 months, compared to less than 12 percent of those who went to see their doctors. The findings serve as a reminder that helping people get healthier doesn’t always require technological advances. Sometimes it may just involve developing more effective ways of getting proven therapy to at-risk communities.


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