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Related Concept Videos

Electrocardiogram01:29

Electrocardiogram

2.2K
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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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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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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Correlation between ECG and Cardiac Cycle01:25

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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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Patient Directed Recording of a Bipolar Three-Lead Electrocardiogram using a Smartwatch with ECG Function
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Artificial intelligence age prediction using electrocardiogram data: Exploring biological age differences.

Shaun Evans1, Sarah A Howson2, Andrew E C Booth1

  • 1Royal Adelaide Hospital, Adelaide, South Australia, Australia; University of Adelaide, Adelaide, South Australia, Australia.

Heart Rhythm
|September 28, 2024
PubMed
Summary
This summary is machine-generated.

Artificial intelligence (AI) can predict biological age from electrocardiograms (ECGs). This AI-ECG model showed younger biological ages in women and older adults, and higher ages in young patients.

Keywords:
CardiologyConvolutional neural networkMachine learningPrognosticationdeep learning

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Area of Science:

  • Cardiology
  • Artificial Intelligence
  • Biomedical Engineering

Background:

  • Artificial intelligence (AI) models trained on electrocardiograms (ECGs) can predict biological age.
  • Biological age prediction from ECGs is a prognostic indicator for mortality and cardiovascular events.

Purpose of the Study:

  • To develop an AI model for predicting biological age from ECGs.
  • To compare baseline characteristics and identify determinants of advanced biological age.

Main Methods:

  • Trained a convolutional neural network AI model on ECGs from 63,246 cardiology inpatients (aged 20-90 years).
  • Validated the model internally (80:20 split) and externally using UK Biobank data.
  • Analyzed performance using correlation, difference, and mean absolute difference metrics.

Main Results:

  • The AI-ECG model achieved a correlation coefficient of 0.72 with a mean absolute age difference of 9.1 years in internal validation.
  • External validation demonstrated similar performance.
  • Significant subgroup differences were observed: young patients (20-29 years) had higher biological ages (14.3 years older), while older patients (80-89 years) had lower biological ages (10.5 years younger).
  • Women and patients with a single ECG were biologically younger than men and those with multiple ECGs, respectively.

Conclusions:

  • AI-ECG-predicted biological age reveals significant differences across patient subgroups.
  • Biological age deviates from chronological age, particularly in young hospitalized patients (older biological age) and older hospitalized patients (younger biological age).
  • Sex and the number of recorded ECGs are associated with differences in biological age.