Continuous monitoring of acute myocardial infarction with a 3-Lead ECG system. (January 2023)

• Record Type: Journal Article
• Authors: Aranda Hernandez, Alfonso; Bonizzi, Pietro; Peeters, Ralf; Karel, Joël
• Is Part Of: Biomedical signal processing and control. Volume 79(2023)Part 1
• Publication Date: 2023-01
• Subjects: Acute myocardial infarction diagnosis -- ECG -- Continuous Monitoring -- Distribution Parameters -- Deep Learning -- RNN; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2022.104041
• Languages: English
• ISSNs: 1746-8094

Abstract

Highlights: We propose a novel method for the continuous ECG monitoring of AMI using a minimal invasive 3-lead ECG system. The method assesses longitudinal trends of ECG and VCG features by analyzing the temporal changes in the properties of their distributions over time. The method is robust against noise, artifacts and perturbations in ECG signals, helping to mitigate the common limitations of ECG-based MI detection methods. The method could be applied in long-term, ambulatory and remote monitoring scenarios. Abstract: 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.