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Related Concept Videos

Cardiopulmonary Resuscitation III: AED Use01:23

Cardiopulmonary Resuscitation III: AED Use

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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...
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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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Cardiomyopathy II: Dilated Cardiomyopathy01:30

Cardiomyopathy II: Dilated Cardiomyopathy

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Dilated cardiomyopathy, or DCM, is a progressive myocardial disorder characterized by ventricular chamber dilation and contractile dysfunction.EtiologyVarious factors can cause DCM, including hypertension and heavy alcohol intake, which contribute to the weakening and enlargement of the heart muscle. Viral infections, such as Coxsackievirus B, adenoviruses, and influenza, can lead to DCM by causing inflammation and damage to heart tissue. Certain chemotherapeutic agents, including daunorubicin,...
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Cardiomyopathy V: Interprofessional Care01:29

Cardiomyopathy V: Interprofessional Care

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Managing cardiomyopathy involves addressing underlying or precipitating causes, treating heart failure with medications, and implementing dietary changes and a balanced exercise and rest regimen.Lifestyle ModificationsCardiomyopathy patients should adopt a low-sodium diet to reduce fluid retention and manage heart failure. A personalized exercise and rest plan helps maintain physical fitness without overstraining the heart. Avoiding alcohol and tobacco is essential to prevent further damage to...
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Pulse rhythm01:30

Pulse rhythm

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Pulse rhythm refers to the pattern of pulsations within specific intervals, offering valuable insights into the regularity or irregularity of the heart's beats as observed through the pattern of pulsation within specific intervals. A regular pulse exhibits a consistent heart rate with uniform waveforms and pulsation force, variations of which can be classified as normal, weak, or bounding.
Conversely, an irregular pulse pattern is termed dysrhythmia, stemming from disruptions in cardiac...
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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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Related Experiment Video

Updated: Aug 22, 2025

Real-Time Cardiac Mapping with a Noninvasive Imageless Electrocardiographic Imaging System
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Real-Time Cardiac Mapping with a Noninvasive Imageless Electrocardiographic Imaging System

Published on: April 11, 2025

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Electrocardiogram-based deep learning improves outcome prediction following cardiac resynchronization therapy.

Philippe C Wouters1, Rutger R van de Leur1, Melle B Vessies1

  • 1Department of Cardiology, University Medical Center Utrecht, Heidelberglaan 100, 3584 CX Utrecht, The Netherlands.

European Heart Journal
|November 7, 2022
PubMed
Summary

A novel deep learning algorithm, FactorECG, accurately predicts cardiac resynchronization therapy (CRT) outcomes using standard ECGs. This explainable AI tool outperforms existing methods, offering personalized predictions without extra clinical data.

Keywords:
Cardiac resynchronization therapyDeep learningElectrocardiogramExplainableHeart failureQRS area

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Benefits of Cardiac Resynchronization Therapy in an Asynchronous Heart Failure Model Induced by Left Bundle Branch Ablation and Rapid Pacing
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Area of Science:

  • Cardiology
  • Artificial Intelligence
  • Medical Diagnostics

Background:

  • Predicting cardiac resynchronization therapy (CRT) outcomes is crucial for patient management.
  • Current electrocardiogram (ECG) criteria have limitations in predicting CRT success.
  • Explainable AI offers potential for improved diagnostic accuracy in cardiology.

Purpose of the Study:

  • To develop and validate an explainable deep learning algorithm (FactorECG) for predicting CRT outcomes.
  • To compare the performance of FactorECG against current ECG guidelines and QRSAREA.
  • To visualize key ECG features identified by the algorithm for clinical decision support.

Main Methods:

  • A deep learning model was trained on over 1.1 million ECGs to extract 21 explainable factors (FactorECG).
  • FactorECG was applied to pre-implantation ECGs of 1306 CRT patients from three academic centers.
  • Performance was evaluated by comparing the prediction of a combined clinical endpoint (death, LVAD, or transplant) using c-statistics.

Main Results:

  • FactorECG achieved a c-statistic of 0.69 (95% CI 0.66-0.72), significantly outperforming QRSAREA (0.61) and guideline criteria (0.57).
  • Key predictors for poor outcome identified by FactorECG include inferolateral T-wave inversion, small right precordial S- and T-wave amplitude, ventricular rate, increased PR interval, and P-wave duration.
  • An online visualization tool was developed for interactive display of ECG features.

Conclusions:

  • FactorECG demonstrates superior discriminative ability for predicting clinical outcomes in CRT patients compared to existing methods.
  • The algorithm requires only a standard 12-lead ECG and does not necessitate additional clinical variables.
  • Automated visualization of ECG features enhances algorithm explainability, potentially facilitating adoption in personalized CRT decision-making.