New AI 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.
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.

"The differences in the heartbeat signal can be very subtle but have massive impact in how you choose to tackle these detections," said Pranav Rajpurkar, a graduate student at Stanford.
To test accuracy of the algorithm, researchers gave a group of three expert cardiologists 300 undiagnosed clips and asked them to reach a consensus about any arrhythmias present in the recordings.
The algorithm could then predict how those cardiologists would label every second of other ECGs with which it was presented, in essence, giving a diagnosis.
The algorithm could be a step toward expert-level arrhythmia diagnosis for people who do not have access to a cardiologist, as in many parts of the developing world and in other rural areas, researchers said.

More immediately, the algorithm could be part of a wearable device that at-risk people keep on at all times that would alert emergency services to potentially deadly heartbeat irregularities as they are happening.