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New artificial intelligence system can quickly detect irregular heartbeats
Stanford scientists, including one of Indian origin, have developed a new artificial intelligence system that can detect life-threatening irregular heartbeats by quickly sifting through hours of heart rhythm data.
Boston: Stanford scientists, including one of Indian origin, have developed a new artificial intelligence system that can detect life-threatening irregular heartbeats by quickly sifting through hours of heart rhythm data.
The algorithm performs better than trained cardiologists, and has the added benefit of being able to sort through data from remote locations where people do not have routine access to cardiologists. The algorithm could bring quick, accurate diagnoses of heart arrhythmias to people without ready access to cardiologists, researchers said.
"One of the big deals about this work, in my opinion, is not just that we do abnormality detection but that we do it with high accuracy across a large number of different types of abnormalities," said Awni Hannun, a graduate student at Stanford University in the US.
People suspected to have an arrhythmia will often get an electrocardiogram (ECG) in a doctor's office. However, if an in-office ECG does not reveal the problem, the doctor may prescribe the patient a wearable ECG that monitors the heart continuously for two weeks.
The resulting hundreds of hours of data would then need to be inspected second by second for any indications of problematic arrhythmias, some of which are extremely difficult to differentiate from harmless heartbeat irregularities. Researchers, led by Andrew Ng from Stanford, set out to develop a deep learning algorithm to detect 13 types of arrhythmia from ECG signals.
In seven months, it was able to diagnose these arrhythmias about as accurately as cardiologists and outperform them in most cases. The researchers believe that this algorithm could someday help make cardiologist-level arrhythmia diagnosis and treatment more accessible to people who are unable to see a cardiologist in person.
The group trained their algorithm on data collected from a wearable ECG monitor. Patients wear a small chest patch for two weeks and carry out their normal day-to-day activities while the device records each heartbeat for analysis. The group took approximately 30,000, 30-second clips from various patients that represented a variety of arrhythmias.
