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Electrocardiogram

Electrocardiogram

An electrocardiogram (ECG or EKG) is a critical diagnostic tool that records the electrical signals produced by the heart during each heartbeat. This recording is achieved through electrodes placed strategically on the arms, legs, and chest. The electrocardiograph amplifies these signals and produces 12 distinct tracings, offering a comprehensive understanding of the heart's electrical activity.
Three major waveforms are present in a typical ECG recording: the P wave, the QRS complex, and...

Electrocardiogram Fundamentals

Electrocardiogram Fundamentals

Introduction
An electrocardiogram (ECG) is a diagnostic tool for identifying cardiac conditions such as arrhythmias, conduction abnormalities, and myocardial ischemia.
Definition
An electrocardiogram (ECG) visualizes the heart's electrical activity by tracing the electrical movement associated with each heartbeat on a graph or monitor. As the heart beats, an electrical wave passes through it, correlating with the cardiac cycle events.
Parts of an ECG
An ECG utilizes electrodes on the skin...

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Clinical trial design data for electrocardiogram artificial intelligence-guided screening for low ejection fraction

Xiaoxi Yao^1,2,3, Rozalina G McCoy^1,4, Paul A Friedman³

¹Division of Health Care Policy and Research, Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA.

|December 24, 2019

Summary

This study introduces materials for the ECG AI-Guided Screening for Low Ejection Fraction (EAGLE) trial. These resources, including a clinician report and data forms, aim to integrate artificial intelligence into routine practice for improved patient screening.

Keywords:

Artificial intelligence Clinical trial Electrocardiogram Heart failure

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Area of Science:

Clinical Trials
Artificial Intelligence in Medicine
Cardiology

Background:

Low ejection fraction (LEF) detection is crucial for cardiovascular health.
Integrating artificial intelligence (AI) into clinical workflows presents challenges.
The EAGLE trial aims to address these challenges through a pragmatic design.

Purpose of the Study:

To detail the materials developed for the ECG AI-Guided Screening for Low Ejection Fraction (EAGLE) trial.
To describe a clinician-facing report designed to translate AI algorithms into routine practice.
To present data collection forms for evaluating AI in clinical settings.

Main Methods:

Development of a clinician-facing action recommendation report using a user-centered, iterative approach with physician input.
Creation of data collection forms tailored for the EAGLE clinical trial.
Design of a pragmatic cluster randomized trial for AI implementation.

Main Results:

The EAGLE trial materials facilitate the translation of AI algorithms into clinical practice.
The developed report and forms support routine screening and alert mechanisms for positive results.
The materials are adaptable for other AI-driven clinical trials.

Conclusions:

The EAGLE trial materials provide a framework for implementing AI-driven screening tools.
User-centered design is key to successful AI integration in healthcare.
These adaptable resources can accelerate the adoption of AI in various clinical research settings.