Development and validation of deep learning ECG-based prediction of myocardial infarction in emergency department patients

Abstract

Myocardial infarction diagnosis is a common challenge in the emergency department. In managed settings, deep learning-based models and especially convolutional deep models have shown promise in electrocardiogram (ECG) classification, but there is a lack of high-performing models for the diagnosis of myocardial infarction in real-world scenarios. We aimed to train and validate a deep learning model using ECGs to predict myocardial infarction in real-world emergency department patients. We studied emergency department patients in the Stockholm region between 2007 and 2016 that had an ECG obtained because of their presenting complaint. We developed a deep neural network based on convolutional layers similar to a residual network. Inputs to the model were ECG tracing, age, and sex; and outputs were the probabilities of three mutually exclusive classes: non-ST-elevation myocardial infarction (NSTEMI), ST-elevation myocardial infarction (STEMI), and control status, as registered in the SWEDEHEART and other registries. We used an ensemble of five models. Among 492,226 ECGs in 214,250 patients, 5,416 were recorded with an NSTEMI, 1,818 a STEMI, and 485,207 without a myocardial infarction. In a random test set, our model could discriminate STEMIs/NSTEMIs from controls with a C-statistic of 0.991/0.832 and had a Brier score of 0.001/0.008. The model obtained a similar performance in a temporally separated test set of the study sample, and achieved a C-statistic of 0.985 and a Brier score of 0.002 in discriminating STEMIs from controls in an external test set. We developed and validated a deep learning model with excellent performance in discriminating between control, STEMI, and NSTEMI on the presenting ECG of a real-world sample of the important population of all-comers to the emergency department. Hence, deep learning models for ECG decision support could be valuable in the emergency department.

Screening for Chagas disease from the electrocardiogram using a deep neural network

“…Deep neural network-enabled analysis of the ECG is a topic of intense research [19]- [25]. Such methods have shown promising potential in detecting diverse conditions that are not traditionally diagnosed from the ECG, such as contractile disfunction [22] or non-STEMI myocardial infarction [19]. ChD is the parasitic disease with the most impact in South America [43] and it affects the lives of millions of individuals worldwide.…”

“…Besides the success of classifying common ECG diagnoses with high-performance [17], [18], the technology has presented successes in predicting and screening for diseases and diagnoses which traditionally were not directly possible only from the ECG. These include detection of myocardial infarction without ST-elevation [19], predicting the future development of atrial fibrillation from sinus rhythm exams [20], [21] and the ability to screen for cardiac contractile dysfunction [22]. Indeed, there is evidence that deep learning reading of ECGs detects more than traditional features, as is indicated by studies showing good prediction of age and even the risk of death [23]- [25].…”

Pediatric ECG-Based Deep Learning to Predict Left Ventricular Dysfunction and Remodeling

“…This ECG is then discarded if any lead is not 2500 samples long or if any lead recording has no lead information (ie, flat line). Given that ECGs are prone to recording errors (eg, baseline wander or electrical interference), a high pass filter was used 24 with a cutoff frequency of 0.8 Hz, rejection band of 0.2 Hz, ripple in passband of 0.5 dB, and attenuation in rejection band of 40 dB. The ECG was then trimmed to 2048 samples (≈8 seconds) to facilitate convenient working with convolution neural networks.…”

“…17 The output of the last block is fed into a fully connected layer with a sigmoid activation function given that outcomes are not mutually exclusive. Similar to other studies, 24 demographic characteristics (ie, age and sex) were also incorporated as inputs along with ECG waveforms in a separate deep learning model (AI-pECG+age+sex). The architecture (Figure S1) was modified by adding a separate part of the model, in which demographic characteristics (ie, age and sex) were concatenated and passed through a fully connected layer.…”

Current and Future Use of Artificial Intelligence in Electrocardiography

“…Even a limb six-lead ECG device can be used for de-tecting myocardial infarction with an area under the receiver operating characteristic curve of 0.88 [100]. An AI model has shown excellent performance in discriminating between control, ST-segment elevation myocardial infarction, and non-ST segment elevation myocardial infarction in a real-world sample of all-comers to an emergency department [101]. AI can be used to reduce door to ECG time in patients with ST-segment elevation myocardial infarction [102].…”