Acute Myocardial Infarction Detection Using Deep Learning-Enabled Electrocardiograms

Usefulness of serial electrocardiograms for diagnosis of acute myocardial infarction.

The purpose of this study was to determine whether the automated detection of acute myocardial infarction (AMI) by utilizing artificial neural networks was improved by using a previous electrocardiogram (ECG) in addition to the current ECG. A total of 4,691 ECGs were recorded from patients admitted to an emergency department due to suspected AMI. Of these, 902 ECGs, in which diagnoses of AMI we...

Rapid Diagnosis of Acute Myocardial Infarction

To improve the specificity of biochemical markers of myocardial infarction (MI) a method to measure cardiac troponin-I (CTn-I) was developed. CTn-I is a protein unique to cardiac muscle and is released after MI. Consecutive 150 patients admitted to the coronary care unit was studied. Value of CTn-I was determined in all samples. CTn-I concentration in the MI patients group was increased com...

Emotional Triggers of Acute Myocardial Infarction

Introduction: Cardiovascular diseases are the leading causes of death around the world. Identification of triggers that lead to acute coronary events in ischemic heart diseases and their prevention can reduce the complications of myocardial infarction. Objective: The purpose of this study is to determine emotional triggers in patients with acute Myocardial Infarction (MI). Materials and Metho...

Concept drift detection in business process logs using deep learning

Process mining provides a bridge between process modeling and analysis on the one hand and data mining on the other hand. Process mining aims at discovering, monitoring, and improving real processes by extracting knowledge from event logs. However, as most business processes change over time (e.g. the effects of new legislation, seasonal effects and etc.), traditional process mining techniques ...

Early Detection of Acute Myocardial Infarction