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Related Concept Videos

Electrocardiogram Fundamentals01:28

Electrocardiogram Fundamentals

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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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Electrocardiogram01:29

Electrocardiogram

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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...
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Correlation between ECG and Cardiac Cycle01:25

Correlation between ECG and Cardiac Cycle

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The electrical signals recorded on an electrocardiogram (ECG) occur before the mechanical processes of contraction and relaxation during the cardiac cycle.
A cardiac action potential originates in the SA node and spreads throughout the atria and the AV node in approximately 0.03 seconds. This results in the P wave in an ECG and triggers atrial contraction. The action potential is then briefly slowed at the AV node, allowing the atria to contract and fill the ventricles with blood before...
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ECG Interpretation of Rhythms01:24

ECG Interpretation of Rhythms

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An electrocardiogram (ECG)graphically represents the heart's electrical activity on ECG paper or a monitor.
Components of the Electrocardiogram
The primary components of a normal ECG waveform in Normal sinus rhythm(NSR) include the P wave, PR interval, QRS complex, ST segment, T wave, and occasionally a U wave.
ECG waveforms are divided by vertical and horizontal lines at standard intervals.
The horizontal axis measures time and rate, and the vertical axis measures amplitude or voltage....
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Real-Time Cardiac Mapping with a Noninvasive Imageless Electrocardiographic Imaging System
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Evolution of single-lead ECG for STEMI detection using a deep learning approach.

C Michael Gibson1, Sameer Mehta2, Mariana R S Ceschim2

  • 1Cardiovascular Division, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA.

International Journal of Cardiology
|November 21, 2021
PubMed
Summary

A new AI algorithm uses single-lead ECGs for faster ST-Elevation Myocardial Infarction (STEMI) detection. This tool shows promise for early diagnosis and improved patient outcomes in acute myocardial infarction cases.

Keywords:
Acute coronary syndromeDeep learningElectrocardiogramWearable devices

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A Research Method For Detecting Transient Myocardial Ischemia In Patients With Suspected Acute Coronary Syndrome Using Continuous ST-segment Analysis
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Area of Science:

  • Cardiology
  • Artificial Intelligence
  • Medical Diagnostics

Background:

  • Symptom-to-door times for ST-Elevation Myocardial Infarction (STEMI) are prolonged due to diagnostic delays.
  • Current diagnostic pathways for STEMI can be time-consuming, impacting patient outcomes.

Purpose of the Study:

  • To develop and validate a machine learning (ML)-guided algorithm for rapid STEMI detection using single-lead electrocardiograms (ECGs).
  • To enhance the speed and accuracy of STEMI diagnosis through an AI-powered approach.

Main Methods:

  • Utilized a large dataset of 8,511 ECGs from the Latin America Telemedicine Infarct Network (LATIN) for model training and validation.
  • Implemented 1-D convolutional neural networks for STEMI detection (STEMI/Not-STEMI) and localization (anterior, inferior, lateral walls).
  • Preprocessed ECG data by detecting QRS complexes and segmenting individual heartbeats for analysis.

Main Results:

  • The AI-guided single-lead ECG strategy achieved 90.5% accuracy for STEMI detection using Lead V2.
  • The STEMI localization model showed promising results for anterior and inferior wall STEMIs, with areas for improvement in lateral wall detection.

Conclusions:

  • AI-enhanced single-lead ECGs represent a viable and accurate screening tool for STEMI.
  • This technology can be integrated into wearable devices, offering a potential pathway for earlier patient treatment and improved myocardial infarction outcomes.