An algorithm developed by researchers from the Stanford Machine Learning Group is able to detect arrhythmias better than certified cardiologists who specialize in the task. After a long process of training the model, it is able to diagnose the potentially deadly abnormal heart rhythms using electrocardiogram (ECG) signals collected from a continuous heart monitoring device manufactured by iRhythm Technologies.
“Having this technology that can tell you the condition of your heart, regardless of where you are, even if you’re not in the hospital constantly being monitored, provides a sense of security,” said Pranav Rajpurkar ’16, a second-year graduate student in the computer science department and co-lead author of the paper.
Arrhythmias can range in severity from requiring no treatment to being life-threatening and are often difficult to accurately diagnose. The diagnostic process can be time consuming, especially when looking through hours of data and also requires the expertise of trained cardiologists. Thus, the algorithm’s efficiency and accuracy in detecting and classifying arrhythmias as one of the 14 rhythm classes is a big step toward the goal of making affordable health care accessible to patients around the world.
“[The ECG] serves as a screening tool for heart-disease, so being able to automate what cardiologists can find in the signal can help us get this tool to people who don’t have it available,” said Awni Hannun, a computer science graduate student and co-lead author of the paper. “It can also help us automate a lot of the current workflow of doctors and technicians. This will free-up more time for them to work on tasks that the machine is not able to do.”
As reported by Stanford News, researchers were led by computer science professor Andrew Ng and worked in collaboration with iRhythm, a digital healthcare company founded by Uday Kumar, a Stanford Biodesign Innovation…