Algorithm diagnoses heart rythm with cardiologist level accuracy

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A new algorithm developed by Stanford computer scientists can isolate through hours of heart rhythm data generated by some wearable monitors. To find sometimes life-threatening irregular heartbeats, called arrhythmias. The algorithm, performs better than trained cardiologists. And has the added benefit of being able to sort through data from remote locations where people don’t have routine access to cardiologists.

People suspected to have an arrhythmia will often get an electrocardiogram (ECG) in a doctor’s office. However, if an in-office ECG doesn’t 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.

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Researchers in the Stanford Machine Learning Group, led by Andrew Ng, an adjunct professor of computer science, saw this as a data problem. They set out to develop a deep learning algorithm to detect 14 types of arrhythmia from ECG signals. They collaborated with the heartbeat monitor company iRhythm to collect. A massive dataset that they used to train a deep neural network model. In seven months, it was able to diagnose these arrhythmias about as accurately as cardiologists and outperform them in most cases.

Building heartbeat interpreter

The group trained their algorithm on data collected from iRhythm’s 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.

To test accuracy of the algorithm, the researchers gave a group of three expert cardiologists 300 undiagnosed clips. Meanwhile, asked them to reach a consensus about any arrhythmias present in the recordings. Working with these annotated clips, 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 group had six different cardiologists, working individually, diagnose the same 300-clip set. The researchers then compared which more closely matched the consensus opinion – the algorithm or the cardiologists working independently. Moreover, found that the algorithm is competitive with the cardiologists, and able to outperform cardiologists on most arrhythmias.

In addition, to cardiologist-level accuracy. The algorithm has the advantage that it does not get fatigued. Can make arrhythmia detections instantaneously and continuously.

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Furthermore,  the group hopes this algorithm could be a step toward expert-level arrhythmia diagnosis. For people who don’t have access to a cardiologist, as in many parts of the developing world and in other rural areas. More immediately, the algorithm could be part of a wearable device that at-risk people. Finally, keep on at all times that would alert emergency services to potentially deadly heartbeat irregularities as they’re happening.

 

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