Using Artificial Intelligence to Catch Irregular Heartbeats
The Zio™ Patch is a long-term cardiac rhythm monitor that provides continuous monitoring for up-to-14 days (significantly longer than the time period of a typical Holter). By providing a longer time period of continuous recording, the Zio™ Patch improves the likelihood of capturing arrhythmias and provides for an equal or higher diagnostic.
Posted on by Dr. Francis Collins
- The iRhythm Zio Patch allows real-world, continuous cardiac monitoring over a 14-day period. Although Holter ECGs are more commonly used in the UK, they are typically limited to between three and seven days monitoring, due to issues around tolerability.
- 2014-1-10 一项采用动态心电图监测可疑性心律失常患者的研究中显示,佩戴新型Zio patch(旧金山公司iRhythm开发)14天,优于佩戴传统Holter动态心电监测仪(Holter monitor,以下简称Holter)24小时。此外,研究显示,在同时接受这两个设备的146名心律失常.
- Wearable Patch Holter Monitoring Traditional 24 hour Holter monitoring has evolved substantially in recent years due to the advent of a wearable 3-14 day long term continuous monitoring or long term Holter monitoring patch. Much of the clinical evidence has concluded that there is substantial clinical value in detecting abnormal cardiac arrhythmias with 3-14 times.
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). Mac address for ps4. 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.
Irhythm Zio Patch Log In
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. Adobe cc master collection mac os x crack included mega. 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.
Ewr 4.2 newest rosters for mac. 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.
Nat Med. 2019 Jan;25(1):65-69.
![Code Code](https://www.mobihealthnews.com/sites/default/files/-ZIO-service-ZIO-patch-for-arrhythmia_0.png)
Links:
Arrhythmia (National Heart, Lung, and Blood Institute/NIH)
![Zio Patch Cardiac Monitoring Zio Patch Cardiac Monitoring](https://ai2-s2-public.s3.amazonaws.com/figures/2017-08-08/d6e47a4110a1c85c72e90919b159826c3d65c151/3-Figure3-1.png)
Video: Artificial Intelligence: Collecting Data to Maximize Potential (NIH)
Zio Patch Instructions For Use
Andrew Ng (Palo Alto, CA)
Zio Patch Cardiac Monitoring Cpt Code
NIH Support: National Heart, Lung, and Blood Institute