Improving Myocardial Infarction Detection via Synthetic ECG Pretraining

TL;DR

This paper introduces a physiology-aware method for generating synthetic ECGs with myocardial infarction features, which enhances deep learning models' ability to detect MI, especially when real data is scarce.

Contribution

The study presents a novel synthetic ECG generation pipeline and demonstrates its effectiveness in pretraining models for improved MI detection performance.

Findings

Synthetic ECGs preserve key morphological features.

Pretraining with synthetic data improves MI classification accuracy.

Performance gains are notable in low-data scenarios.

Abstract

Myocardial infarction is a major cause of death globally, and accurate early diagnosis from electrocardiograms (ECGs) remains a clinical priority. Deep learning models have shown promise for automated ECG interpretation, but require large amounts of labeled data, which are often scarce in practice. We propose a physiology-aware pipeline that (i) synthesizes 12-lead ECGs with tunable MI morphology and realistic noise, and (ii) pre-trains recurrent and transformer classifiers with self-supervised masked-autoencoding plus a joint reconstruction-classification objective. We validate the realism of synthetic ECGs via statistical and visual analysis, confirming that key morphological features are preserved. Pretraining on synthetic data consistently improved classification performance, particularly in low-data settings, with AUC gains of up to 4 percentage points. These results show that controlled synthetic ECGs can help improve MI detection when real clinical data is limited.