Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand.
Physiol Meas. 2018 Sep 24;39(9):094006. doi: 10.1088/1361-6579/aad9ed.
The electrocardiogram (ECG) provides an effective, non-invasive approach for clinical diagnosis in patients with cardiac diseases such as atrial fibrillation (AF). AF is the most common cardiac rhythm disturbance and affects ~2% of the general population in industrialized countries. Automatic AF detection in clinics remains a challenging task due to the high inter-patient variability of ECGs, and unsatisfactory existing approaches for AF diagnosis (e.g. atrial or ventricular activity-based analyses).
We have developed RhythmNet, a 21-layer 1D convolutional recurrent neural network, trained using 8528 single-lead ECG recordings from the 2017 PhysioNet/Computing in Cardiology (CinC) Challenge, to classify ECGs of different rhythms including AF automatically. Our RhythmNet architecture contained 16 convolutions to extract features directly from raw ECG waveforms, followed by three recurrent layers to process ECGs of varying lengths and to detect arrhythmia events in long recordings. Large 15 × 1 convolutional filters were used to effectively learn the detailed variations of the signal within small time-frames such as the P-waves and QRS complexes. We employed residual connections throughout RhythmNet, along with batch-normalization and rectified linear activation units to improve convergence during training.
We evaluated our algorithm on 3658 testing data and obtained an F accuracy of 82% for classifying sinus rhythm, AF, and other arrhythmias. RhythmNet was also ranked 5th in the 2017 CinC Challenge.
Potentially, our approach could aid AF diagnosis in clinics and be used for patient self-monitoring to improve the early detection and effective treatment of AF.
心电图（ECG）为心脏病患者的临床诊断提供了一种有效、非侵入性的方法，例如心房颤动（AF）。AF 是最常见的心律失常，在工业化国家影响约 2%的普通人群。由于 ECG 之间的个体差异很大，以及现有的 AF 诊断方法（例如基于心房或心室活动的分析）不尽人意，因此在临床上自动检测 AF 仍然是一项具有挑战性的任务。
我们开发了 RhythmNet，这是一个 21 层 1D 卷积递归神经网络，使用来自 2017 年 PhysioNet/计算心脏病学挑战赛（CinC）的 8528 个单导联 ECG 记录进行训练，用于自动分类不同节律的 ECG，包括 AF。我们的 RhythmNet 架构包含 16 个卷积，可直接从原始 ECG 波形中提取特征，然后使用三个递归层处理不同长度的 ECG，并在长记录中检测心律失常事件。使用大的 15×1 卷积滤波器可以有效地学习信号在小时间帧（例如 P 波和 QRS 复合体）内的详细变化。我们在整个 RhythmNet 中使用了残差连接，以及批量归一化和修正线性激活单元，以在训练期间提高收敛速度。
我们在 3658 个测试数据上评估了我们的算法，并获得了 82%的正弦节律、AF 和其他心律失常分类的 F 准确性。RhythmNet 在 2017 年 CinC 挑战赛中也排名第五。
我们的方法有可能有助于临床 AF 诊断，并可用于患者自我监测，以提高 AF 的早期检测和有效治疗。
