Name
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. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.23919/CinC49843.2019.9005742 | 2019 Computing in Cardiology (CinC) |
Keywords | DocType | ISSN |
dynamic assessment,ECG parameter distribution,distribution properties,AMI condition,continuous monitoring setting,false positives,no AMI,acute myocardial infarction diagnosis,ECG-based features,acute myocardial infarction detection | Conference | 2325-8861 |
ISBN | Citations | PageRank |
978-1-7281-5942-3 | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Alfonso Aranda | 1 | 0 | 0.34 |
| Joël M. H. Karel | 2 | 0 | 0.34 |
| pietro bonizzi | 3 | 8 | 5.81 |
| Ralf L. M. Peeters | 4 | 62 | 22.61 |