Acute MI Detection Derived From ECG Parameters Distribution

Abstract

Several studies in the past have evaluated the use of different ECG-based features to diagnose acute myocardial infarction (AMI). This was generally done by looking at how well a feature reflects differences between baseline (no AMI) and AMI situations. This approach tends to overlook the progress of AMI and to underestimate false positives when implemented into a continuous monitoring setting and therefore appears inadequate for it. This has hindered the adoption of those methods in the clinical practice. In this research, we present a novel set of parameters for the dynamic assessment of AMI condition. Those parameters are obtained by analyzing the changes over time in the distribution properties of ECG-based features.