Abstract 4125281: Detection of Acute Myocardial Infarction and Ischemia From Lead-I ECG Using Deep Learning

Introduction: While smartwatches that acquire Lead-I electrocardiogram (ECG) have shown promise in detecting arrhythmias, the detection of ischemic events using Lead-I alone remains understudied. We aim to build and evaluate a deep-learning model to determine whether Lead-I ECG can identify ischemia/infarction in a 12-lead ECG. Methods: We developed a deep-learning model that can identify MI/ischemia using only Lead-I ECG signal as input. For this task, we utilized PTB-XL, a large publicly available dataset of 12-Lead clinical ECG with associated diagnostic labels. Our experiment utilized 8781 ECGs whose labels were validated by at-least one cardiologist with 100% confidence scores. This dataset includes 1391 ECGs that are labeled as either ischemic ST/T changes or MI, and 7390 ECGs with either “normal” labels or non-ischemic ST/T abnormalities. We split the dataset in training, validation, and holdout sets (80-10-10) with equal stratification of the diagnosis classes. The holdout test set contains 909 non-ischemic ECGs and 233 ECGs with MI/ischemia diagnosis, 106 of which are labeled as acute MI or ischemic ST/T changes and 127 labeled as other MI chronicity. The discriminatory ability of the model is evaluated with the Area Under the receiver-operating Curve (AUC), sensitivity, specificity, and the predictive values. We further analyze the model’s capability in discriminating the cohort of acute MI and ischemia diagnoses from other MI as well as from the non-ischemic cohort. Results: The model has an AUC of 0.88 for discriminating an MI/ischemia vs normal. Using a threshold corresponding to 80% sensitivity, the corresponding specificity is 79% and the negative and positive predictive values are 94% and 50% (Table-1). Although the model was trained to identify the combined class of MI/ischemia, it can differentiate acute MI and ischemia cohorts from other MI as well as from non-ischemic diagnoses with p-values<10 -4 (Table-2). Lastly, with the same threshold, the sensitivity for detecting acute inferior myocardial infarctions or inferior ischemia – a class that is difficult to detect with Lead-I alone – is 82%. Conclusions: Our deep learning model, which was trained on Lead-I ECG alone, can effectively detect acute MI and ischemia that would have been identified using a 12-lead ECG. This method presents a proof-of-concept for applications in home settings. Further prospective validation using wearable ECG monitors that record lead-I is warranted.