Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network
Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network
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使用深度神经网络在动态心电图中进行心脏病学家水平的心律失常检测和分类
DOI：
10.1038/s41591-018-0268-3
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发表时间：
2019-01-01
期刊：
影响因子：
82.9
通讯作者：
Ng, Andrew Y.
中科院分区：
文献类型：
--
作者：
Hannun, Awni Y.;Rajpurkar, Pranav;Ng, Andrew Y.
计算机心电判读在临床心电工作流程中起着至关重要的作用(1)。广泛可用的数字心电数据和深度学习的算法范例(2)为大幅度提高自动心电分析的准确性和可扩展性提供了机会。然而，对跨各种诊断类别的ECG分析的端到端深度学习方法的全面评估尚未有报道。在这里，我们开发了一个深度神经网络（DNN），利用来自53,549名使用单导联动态心电图监测设备的患者的91,232张单导联心电图对12个节律类别进行分类。当针对独立测试数据集进行验证时，由董事会认证的执业心脏病学家共识委员会注释，DNN在受试者工作特征曲线（ROC）下的平均面积为0.97。DNN的平均F-1评分（即阳性预测值和敏感性的调和平均值）（0.837）超过了普通心脏病专家（0.780）。特异性固定在心脏病专家达到的平均特异性上，DNN的敏感性超过了心脏病专家对所有节律类别的平均敏感性。这些发现表明，端到端深度学习方法可以从单导联心电图中对各种不同的心律失常进行分类，具有与心脏病专家相似的高诊断性能。如果在临床环境中得到证实，这种方法可以通过准确分诊或优先处理最紧急的情况，降低计算机心电解释的误诊率，提高专家心电解释的效率。
Computerized electrocardiogram (ECG) interpretation plays a critical role in the clinical ECG workflow(1). Widely available digital ECG data and the algorithmic paradigm of deep learning(2) present an opportunity to substantially improve the accuracy and scalability of automated ECG analysis. However, a comprehensive evaluation of an end-to-end deep learning approach for ECG analysis across a wide variety of diagnostic classes has not been previously reported. Here, we develop a deep neural network (DNN) to classify 12 rhythm classes using 91,232 single-lead ECGs from 53,549 patients who used a single-lead ambulatory ECG monitoring device. When validated against an independent test dataset annotated by a consensus committee of board-certified practicing cardiologists, the DNN achieved an average area under the receiver operating characteristic curve (ROC) of 0.97. The average F-1 score, which is the harmonic mean of the positive predictive value and sensitivity, for the DNN (0.837) exceeded that of average cardiologists (0.780). With specificity fixed at the average specificity achieved by cardiologists, the sensitivity of the DNN exceeded the average cardiologist sensitivity for all rhythm classes. These findings demonstrate that an end-to-end deep learning approach can classify a broad range of distinct arrhythmias from single-lead ECGs with high diagnostic performance similar to that of cardiologists. If confirmed in clinical settings, this approach could reduce the rate of misdiagnosed computerized ECG interpretations and improve the efficiency of expert human ECG interpretation by accurately triaging or prioritizing the most urgent conditions.