[论文解读] Automatic Diagnosis of the Short-Duration 12-Lead ECG using a Deep Neural Network: the CODE Study.

Antônio H. Ribeiro, Antônio H. Ribeiro+10 more|arXiv (Cornell University)|Apr 2, 2019

ECG Monitoring and Analysis参考文献 34被引用 8

一句话总结

本研究开发了一种深度残差神经网络，利用包含超过200万份临床记录的大规模真实世界数据集，从短时程（7–10秒）心电图导联中自动诊断六种12导联心电图异常。该模型的F1分数超过80%，特异性超过99%，在诊断准确性上优于医学住院医师和医学生。

ABSTRACT

We present a Deep Neural Network (DNN) model for predicting electrocardiogram (ECG) abnormalities in short-duration 12-lead ECG recordings. The analysis of the digital ECG obtained in a clinical setting can provide a full evaluation of the cardiac electrical activity and have not been studied in an end-to-end machine learning scenario. Using the database of the Telehealth Network of Minas Gerais, under the scope of the CODE (Clinical Outcomes in Digital Electrocardiology) study, we built a novel dataset with more than 2 million ECG tracings, orders of magnitude larger than those used in previous studies. Moreover, our dataset is more realistic, as it consists of 12-lead ECGs recorded during standard in-clinic exams. Using this data, we trained a residual neural network with 9 convolutional layers to map ECG signals with a duration of 7 to 10 seconds into 6 different classes of ECG abnormalities. High-performance measures were obtained for all ECG abnormalities, with F1 scores above $80\%$ and specificity indexes over $99\%$. We compare the performance with cardiology and emergency resident medical doctors as well as medical students and, considering the F1 score, the DNN matches or outperforms the medical residents and students for all abnormalities. These results indicate that end-to-end automatic ECG analysis based on DNNs, previously used only in a single-lead setup, generalizes well to the 12-lead ECG. This is an important result in that it takes this technology much closer to standard clinical practice.

研究动机与目标

• 开发一种端到端的深度神经网络，用于在短时程12导联心电图中自动诊断心电图异常。
• 解决机器学习应用中12导联心电图分析缺乏大规模真实世界数据集的问题。
• 评估该深度神经网络（DNN）与临床专业人员（包括心脏病学住院医师和医学生）的性能对比。
• 证明在真实临床12导联心电图上训练的深度学习模型能够有效泛化至临床实践。

提出的方法

• 该模型采用包含9个卷积层的残差神经网络，处理时长为7–10秒的12导联心电图信号。
• 网络在来自米纳斯吉拉斯州远程医疗网络的超过200万份12导联心电图导联的新型数据集上进行训练。
• 训练数据源自CODE（数字心电图临床结局）研究，确保临床真实性和可扩展性。
• 通过端到端学习，将原始心电图信号直接映射到六个预定义的心电图异常类别。
• 采用标准指标（包括F1分数和特异性）评估性能，并与人类专家进行比较。

实验结果

研究问题

• RQ1 深度神经网络能否在使用真实世界临床数据的情况下，实现对短时程12导联心电图的高诊断性能？
• RQ2 该DNN在诊断心电图异常方面的表现与医学住院医师和医学生相比如何？
• RQ3 端到端深度学习在临床环境中，从单导联到多导联心电图分析的泛化程度如何？
• RQ4 大规模真实世界12导联心电图数据集在多大程度上能提升自动化心电图诊断模型的鲁棒性和临床相关性？

主要发现

• 该DNN在所有六种心电图异常类别中均实现了超过80%的F1分数，表明其具有强大的诊断性能。
• 所有异常的特异性均超过99%，表明其在识别正常心电图方面具有高精度。
• 在所有心电图异常类别中，该模型的F1分数与医学住院医师和医学生相当或更优。
• 结果表明，基于真实12导联心电图数据训练的端到端深度学习模型能够有效泛化至临床实践。
• 本研究证明了利用大规模真实世界心电图数据训练高性能自动化诊断系统是可行的。