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Related Concept Videos

Electrocardiogram01:29

Electrocardiogram

3.5K
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...
3.5K
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...
913
ECG Interpretation of Rhythms01:24

ECG Interpretation of Rhythms

5.0K
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....
5.0K
ECG Interpretation of Arrhythmias II: Atrial, Junctional and Ventricular Arrhythmias01:25

ECG Interpretation of Arrhythmias II: Atrial, Junctional and Ventricular Arrhythmias

191
Arrhythmia is a condition characterized by an irregular heart rhythm, with ECG changes that differ based on its origin and nature. The types of arrhythmias discussed below include atrial, junctional, and ventricular arrhythmias.Atrial ArrhythmiasPremature Atrial Complexes (PACs): PACs are early atrial beats caused by stress, caffeine, alcohol, electrolyte imbalances, hypoxia, hyperthyroidism, or certain medications (e.g., bronchodilators and decongestants). The ECG shows early P waves with an...
191
Cardiopulmonary Resuscitation III: AED Use01:23

Cardiopulmonary Resuscitation III: AED Use

101
Introduction to AEDAn Automated External Defibrillator (AED) is a portable medical device that analyzes the heart's rhythm and, if necessary, delivers an electrical shock to help the heart re-establish an effective rhythm during sudden cardiac arrest (SCA). SCA occurs when the heart suddenly and unexpectedly stops beating, leading to a loss of blood flow to the brain and other vital organs. In such emergencies, time is of the essence, and using an AED, combined with Cardiopulmonary...
101
Correlation between ECG and Cardiac Cycle01:25

Correlation between ECG and Cardiac Cycle

9.0K
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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Related Experiment Video

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Analyzing Long-Term Electrocardiography Recordings to Detect Arrhythmias in Mice
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A comprehensive artificial intelligence-enabled electrocardiogram interpretation program.

Anthony H Kashou1, Wei-Yin Ko2, Zachi I Attia2

  • 1Department of Medicine, Mayo Clinic, Rochester, Minnesota.

Cardiovascular Digital Health Journal
|March 10, 2022
PubMed
Summary

An artificial intelligence-enabled electrocardiogram (AI-ECG) algorithm shows high accuracy in interpreting 12-lead ECGs, comparable to cardiologists. This AI-ECG tool could improve diagnostic consistency and workflow efficiency in clinical practice.

Keywords:
Artificial intelligenceConvolutional neural networkDeep learningECGElectrocardiogramElectrocardiographyMachine learning

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Area of Science:

  • Cardiology
  • Artificial Intelligence
  • Medical Diagnostics

Background:

  • Automated electrocardiogram (ECG) interpretation algorithms aim to improve accuracy and efficiency but often show inconsistent performance.
  • Existing computer algorithms struggle with the complexity of comprehensive 12-lead ECG interpretation.

Purpose of the Study:

  • To develop and validate an artificial intelligence-enabled ECG (AI-ECG) algorithm for comprehensive 12-lead ECG interpretation.
  • To achieve diagnostic accuracy comparable to practicing cardiologists.

Main Methods:

  • A convolutional neural network was used to create a multilabel classifier for assessing 66 discrete ECG diagnostic codes.
  • A large dataset of 2,499,522 ECGs from 720,978 patients, collected between 1993 and 2017, was randomly divided into training, validation, and testing sets.
  • The AI-ECG algorithm's performance was compared against cardiologist interpretations using receiver operating characteristic (ROC) and precision recall (PR) curves.

Main Results:

  • The AI-ECG algorithm achieved an area under the ROC curve of ≥0.98 for 62 out of 66 diagnostic codes.
  • The model demonstrated high performance across various categories including rhythm, conduction, ischemia, and waveform morphology.
  • Precision recall metrics indicated a sensitivity of ≥95% for all assessed ECG codes, even with category imbalance.

Conclusions:

  • The developed AI-ECG algorithm exhibits high diagnostic performance, rivaling that of expert cardiologists.
  • AI-ECG reading tools offer potential for scalable and consistent ECG interpretation as the technology becomes more widespread.