Cardiologist-Level Arrhythmia Detection with Convolutional Neural Networks
This work addresses the challenge of accurate and scalable arrhythmia detection for patients using wearable monitors, representing a strong specific gain rather than an incremental improvement.
The researchers tackled the problem of detecting heart arrhythmias from single-lead ECG data using a convolutional neural network, achieving performance that exceeded the average of six board-certified cardiologists in both recall and precision.
We develop an algorithm which exceeds the performance of board certified cardiologists in detecting a wide range of heart arrhythmias from electrocardiograms recorded with a single-lead wearable monitor. We build a dataset with more than 500 times the number of unique patients than previously studied corpora. On this dataset, we train a 34-layer convolutional neural network which maps a sequence of ECG samples to a sequence of rhythm classes. Committees of board-certified cardiologists annotate a gold standard test set on which we compare the performance of our model to that of 6 other individual cardiologists. We exceed the average cardiologist performance in both recall (sensitivity) and precision (positive predictive value).