Automatic Triage of 12-Lead ECGs Using Deep Convolutional Neural Networks

Abstract

BACKGROUND The correct interpretation of the ECG is pivotal for the accurate diagnosis of many cardiac abnormalities, and conventional computerized interpretation has not been able to reach physician-level accuracy in detecting (acute) cardiac abnormalities. This study aims to develop and validate a deep neural network for comprehensive automated ECG triage in daily practice. METHODS AND RESULTS We developed a 37-layer convolutional residual deep neural network on a data set of free-text physician-annotated 12-lead ECGs. The deep neural network was trained on a data set with 336.835 recordings from 142.040 patients and validated on an independent validation data set (n=984), annotated by a panel of 5 cardiologists electrophysiologists. The 12-lead ECGs were acquired in all noncardiology departments of the University Medical Center Utrecht. The algorithm learned to classify these ECGs into the following 4 triage categories: normal, abnormal not acute, subacute, and acute. Discriminative performance is presented with overall and category-specific concordance statistics, polytomous discrimination indexes, sensitivities, specificities, and positive and negative predictive values. The patients in the validation data set had a mean age of 60.4 years and 54.3% were men. The deep neural network showed excellent overall discrimination with an overall concordance statistic of 0.93 (95% CI, 0.92-0.95) and a polytomous discriminatory index of 0.83 (95% CI, 0.79-0.87). CONCLUSIONS This study demonstrates that an end-to-end deep neural network can be accurately trained on unstructured free-text physician annotations and used to consistently triage 12-lead ECGs. When further fine-tuned with other clinical outcomes and externally validated in clinical practice, the demonstrated deep learning-based ECG interpretation can potentially improve time to treatment and decrease healthcare burden.

Keywords: deep learning; deep neural networks; electrocardiography; triage.

Figure 1. ECG diagnoses with their corresponding triage categories.

Triage categories as defined by the panel of electrophysiologists, with (1) normal, (2) not acute abnormal (consultation without priority), (3) subacute abnormal (consultation with some priority), and (4) acute abnormal (consult immediately). The ECG diagnoses derived from the text‐mining algorithm were used to categorize the training data using these rules. When multiple diagnoses were given, the final triage category was the maximum category. AV indicates atrioventricular; AVNRT, atrioventricular nodal reentrant tachycardia; and AVRT, atrioventricular reentrant tachycardia.

Figure 2. Overview of the labeling into triage categories in the training and validation data sets.

The training labels (left), used for training, are derived from the free‐text annotation given to the ECG by a single physician in daily practice. The ECG diagnoses are mapped to triage categories using the rules defined by a panel of electrophysiologists (Figure 1). The validation labels (right), used for validation of the deep neural network, are given by the expert panel based on visual inspection of a 12‐lead ECG.

Figure 3. Confusion matrix for the deep neural network. Rows represent the categories given by the reference standard (expert panel), and columns represent the categories predicted by the deep neural network.

The color map is normalized per row and represents the percentage in the true triage category.

Figure 4

Examples of ECG leads II and V1 with a superimposed guided gradient‐weighted class activation mapping visualization showing regions important for the deep neural network to predict a certain triage category. A, Normal ECG with focus on the P‐wave, QRS‐complex, and T‐wave. B, Normal ECG with a single ignored premature ventricular complex. C, Subacute ECG with a long QT interval and a focus on the beginning and end of the QT‐segment. D, Acute ECG with an inferior ST‐segment–elevation myocardial infarction and a focus on the ST‐segment and J‐point. E, Acute ECG with a junctional escape rhythm and a focus on the pre‐QRS‐segment, where the P‐wave is missing. The full 12‐lead ECGs are available in Figures S2 through S6.