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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


Taking Microfluidics to New Lengths

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Fiber Microfluidics

Caption: Microfluidic fiber sorting a solution containing either live or dead cells. The type of cell being imaged and the real time voltage (30v) is displayed at bottom. It is easy to imagine how this could be used to sort a mixture of live and dead cells. Credit: Yuan et al., PNAS

Microfluidics—the manipulation of fluids on a microscopic scale— has made it possible to produce “lab-on-a-chip” devices that detect, for instance, the presence of Ebola virus in a single drop of blood. Now, researchers hope to apply the precision of microfluidics to a much broader range of biomedical problems. Their secret? Move the microlab from chips to fibers.

To do this, an NIH-funded team builds microscopic channels into individual synthetic polymer fibers reaching 525 feet, or nearly two football fields long! As shown in this video, the team has already used such fibers to sort live cells from dead ones about 100 times faster than current methods, relying only on natural differences in the cells’ electrical properties. With further design and development, the new, fiber-based systems hold great promise for, among other things, improving kidney dialysis and detecting metastatic cancer cells in a patient’s bloodstream.


Wearable Ultrasound Patch Monitors Blood Pressure

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Placement of the blood pressure patch

Caption: Worn on the neck, the device records central blood pressure in the carotid artery (CA), internal jugular vein (Int JV) and external jugular vein (Ext JV).
Credit: Adapted from Wang et al, Nature Biomedical Engineering

There’s lots of excitement out there about wearable devices quietly keeping tabs on our health—morning, noon, and night. Most wearables monitor biological signals detectable right at the surface of the skin. But, the sensing capabilities of the “skin” patch featured here go far deeper than that.

As described recently in Nature Biomedical Engineering, when this small patch is worn on the neck, it measures blood pressure way down in the central arteries and veins more than an inch beneath the skin [1]. The patch works by emitting continuous ultrasound waves that monitor subtle, real-time changes in the shape and size of pulsing blood vessels, which indicate rises or drops in pressure.


Building a Smarter Bandage

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Smart Bandage

Credit: Tufts University, Medford, MA

Smartphones, smartwatches, and smart electrocardiograms. How about a smart bandage?

This image features a prototype of a smart bandage equipped with temperature and pH sensors (lower right) printed directly onto the surface of a thin, flexible medical tape. You also see the “brains” of the operation: a microprocessor (upper left). When the sensors prompt the microprocessor, it heats up a hydrogel heating element in the bandage, releasing drugs and/or other healing substances on demand. It can also wirelessly transmit messages directly to a smartphone to keep patients and doctors updated.

While the smart bandage might help mend everyday cuts and scrapes, it was designed with the intent of helping people with hard-to-heal chronic wounds, such as leg and foot ulcers. Chronic wounds affect millions of Americans, including many seniors [1]. Such wounds are often treated at home and, if managed incorrectly, can lead to infections and potentially serious health problems.


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.


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