The researchers partnered with iRhythm, a company that makes portable ECG devices. They collected 30,000 30-second clips from patients with different forms of arrhythmia. To assess the accuracy of their algorithm, the team compared its performance to that of five different cardiologists on 300 undiagnosed clips. They had a panel of three expert cardiologists provide a ground-truth judgment.
Deep learning involves feeding large quantities of data into a big simulated neural network, and fine-tuning its parameters until it accurately recognized problematic ECG signals. The approach has proven adept at identifying complex patterns in images and audio, and it has led to the development of better-than-human image-recognition and voice-recognition systems.
Eric Horvitz, managing director of Microsoft Research and both a medical doctor and an expert on machine learning, says others, including two different groups from MIT and the University of Michigan, are applying machine learning to the detection of heart arrhythmias.