The role of automatic electrocardiogram (ECG) analysis in clinical practice
is limited by the accuracy of existing models. Deep Neural Networks (DNNs) are
models composed of stacked transformations that learn tasks by examples. This
technology has recently achieved striking success in a variety of task and
there are great expectations on how it might improve clinical practice. Here we
present a DNN model trained in a dataset with more than 2 million labeled exams
analyzed by the Telehealth Network of Minas Gerais and collected under the
scope of the CODE (Clinical Outcomes in Digital Electrocardiology) study. The
DNN outperform cardiology resident medical doctors in recognizing 6 types of
abnormalities in 12-lead ECG recordings, with F1 scores above 80% and
specificity over 99%. These results indicate ECG analysis based on DNNs,
previously studied in a single-lead setup, generalizes well to 12-lead exams,
taking the technology closer to the standard clinical practice.

使用超过 200 万标记心电图的数据训练的深度神经网络模型，比心脏病学住院医生在识别 12 导联心电图上准确率高，F1 分数超过 80％且特异性高于 99％，这表明基于 DNN 的 ECG 分析可推广到 12 导联心电图上，可应用于标准临床实践中。