#Heart monitor patch how to
#Heart monitor patch Patch

In this way, computers can learn to analyze and sort highly complex data using both more obvious and hidden features. 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. The network is typically composed of multiple information-processing layers, which operate on the data and compute increasingly complex and abstract representations. As they begin to вЂњseeвЂќ complex relationships, some connections in the network are strengthened while others are weakened. In deep learning, computers look for patterns in data. Deep learning is inspired by how our own brainвЂ™s neural networks process information, learning to focus on some details but not others. The accuracy improves as the machine вЂњseesвЂќ more data.Д«ut the teamвЂ™s real interest was in utilizing a special class of machine learning called deep neural networks, or deep learning.

#Heart monitor patch how to

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

#Heart monitor patch Patch

The wireless patch continuously monitors heart rhythms, storing EKG data for later analysis. ItвЂ™s water resistant and can be kept on around the clock while a person sleeps, exercises, or takes a shower. The Zio patch is a 2-by-5-inch adhesive patch, worn much like a bandage, on the upper left side of the chest. 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. As published in Nature Medicine, the Stanford team started by assembling a large EKG dataset from more than 53,000 people. HereвЂ™s where the team, led by computer scientists Awni Hannun and Andrew Ng, Stanford University, Palo Alto, CA, saw an AI opportunity. ThatвЂ™s not always easy to do with current methods.

This is an important arrhythmia to detect, even if it may only be present occasionally over many days of monitoring. The precise, wave-like features of the electrical impulses allow doctors to determine whether a personвЂ™s heart is beating normally.ДЇor example, in people with AFib, the heartвЂ™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.

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.Д®KG tests measure electrical impulses in the heart, which signal the heart muscle to contract and pump blood to the rest of the body. The computer became so вЂњsmartвЂќ that it could classify 10 different types of irregular heart rhythms, including atrial fibrillation (AFib). Now, NIH-funded researchers have found that artificial intelligence (AI) can help.ДЄ powerful computer вЂњstudiedвЂќ more than 90,000 EKG recordings, from which it вЂњlearnedвЂќ to recognize patterns, form rules, and apply them accurately to future EKG readings. (IвЂ™m glad to say I am in normal sinus rhythm.)ДЇor 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. In fact, my Apple Watch makes it possible to record a real-time EKG whenever I want. Thanks to advances in wearable health technologies, itвЂ™s now possible for people to monitor their heart rhythms at home for days, weeks, or even months via wireless electrocardiogram (EKG) patches. Francis Collins Credit: gettyimages/enot-poloskun Using Artificial Intelligence to Catch Irregular Heartbeats