With tens of thousands of electrocardiogram (ECG) records processed by mobile
cardiac event recorders every day, heart rhythm classification algorithms are
an important tool for the continuous monitoring of patients at risk. We utilise
an annotated dataset of 12,186 single-lead ECG recordings to build a diverse
ensemble of recurrent neural networks (RNNs) that is able to distinguish
between normal sinus rhythms, atrial fibrillation, other types of arrhythmia
and signals that are too noisy to interpret. In order to ease learning over the
temporal dimension, we introduce a novel task formulation that harnesses the
natural segmentation of ECG signals into heartbeats to drastically reduce the
number of time steps per sequence. Additionally, we extend our RNNs with an
attention mechanism that enables us to reason about which heartbeats our RNNs
focus on to make their decisions. Through the use of attention, our model
maintains a high degree of interpretability, while also achieving
state-of-the-art classification performance with an average F1 score of 0.79 on
an unseen test set (n=3,658).

利用带有注释的 12,186 个单引导 ECG 记录数据集，我们构建了一种多样化的循环神经网络 (RNNs) 集成，以区分正常窦性心律、房颤、其他类型的心律失常和信号过于嘈杂难以解释。通过注意力机制，使我们的模型具有高度可解释性，并在看不见的测试集（n = 3,658）上实现了 0.79 的平均 F1 得分，同时也达到了最先进的分类性能。