Continuous monitoring of acute myocardial infarction with a 3-Lead ECG system

Objective: A growing body of research focuses on the automated diagnosis of acute myocardial infarction (AMI) using electrocardiogram (ECG) recordings. Several methods rely on differences between the ECG at baseline (no AMI) and during AMI condition. However, this approach may not sufficiently account for the progress of AMI, and it can underestimate the effect of false positives in a continuous monitoring setting. This in turn may hinder the adoption of automated methods for AMI diagnosis in the clinical practice. In this study, we propose a new automated method for the dynamic assessment of AMI condition. This method accounts for the dynamic nature underlying AMI events and the need for a low false positives incidence. Using a reduced 3-lead ECG system, we developed a novel set of parameters able to capture changes over time in the distribution properties of ECG -derived features. These parameters are used to train and validate a deep learning model in order to perform dynamic assessment of AMI condition. Conclusion: Results suggest that the proposed method is able to capture the dynamic evolution of AMI with a false positive rate below 1%. Significance: Thanks to the reduced number of leads, the proposed method could be used to assess AMI condition in long-term, remote and home monitoring, and intensive care unit (ICU) environments.

Automated summary

This study proposes a new automated method for the dynamic assessment of acute myocardial infarction (AMI) condition using electrocardiogram (ECG) recordings. The method utilizes a reduced 3-lead ECG system and a novel set of parameters to capture changes over time in the distribution properties of ECG-derived features. Results indicate that the proposed method can accurately capture the dynamic evolution of AMI with a low false positive rate. The method's reduced number of leads makes it suitable for long-term, remote, home monitoring, and intensive care unit (ICU) environments.