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).

使用单导联可穿戴监测器记录的心电图，开发了一种算法，可以检测出广泛类型的心律失常，其表现超过了获得认证的心脏病专家。通过 34 层的卷积神经网络，将心电图样本序列映射到节律类别序列，并将其与 6 位独立心脏病专家的性能进行比较。结果表明，该算法在感知率和阳性预测价值等方面均优于平均心脏病专家。