Novel ECG features and machine learning to optimize culprit lesion detection in patients with suspected acute coronary syndrome-Reference-Cited by-同舟云学术

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Novel ECG features and machine learning to optimize culprit lesion detection in patients with suspected acute coronary syndrome

Published:2021-11 Issue: Volume:69 Page:31-37
ISSN:0022-0736
Container-title:Journal of Electrocardiology
language:en
Short-container-title:Journal of Electrocardiology

Author:

Bouzid Zeineb,Faramand Ziad,Gregg Richard E.,Helman Stephanie,Martin-Gill Christian,Saba Samir,Callaway Clifton,Sejdić Ervin,Al-Zaiti Salah

Funder

National Institutes of Health

University of Pittsburgh

Publisher

Elsevier BV

Subject

Cardiology and Cardiovascular Medicine

Reference16 articles.

1. 2014 AHA/ACC guideline for the management of patients with non–ST-elevation acute coronary syndromes: executive summary: a report of the American College of Cardiology/American Heart Association task force on practice guidelines;Amsterdam;Circulation.,2014

2. 2013 ACCF/AHA guideline for the management of ST-elevation myocardial infarction: a report of the American College of Cardiology Foundation/American Heart Association task force on practice guidelines;O’gara;J Am Coll Cardiol,2013

3. Electrocardiographic diagnosis of ST segment elevation myocardial infarction: an evaluation of three automated interpretation algorithms;Garvey;J Electrocardiol,2016

4. The role of the ECG in diagnosis, risk estimation, and catheterization laboratory activation in patients with acute coronary syndromes: a consensus document;Birnbaum;Ann Noninvasive Electrocardiol,2014

5. Non-ST-segment elevation myocardial infarction: a novel and robust approach for early detection of patients at risk;Lux;J Am Heart Assoc,2015

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