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Related Concept Videos

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.
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Heart Failure IV: Classification and Diagnostic Evaluation01:30

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Heart failure can be classified in various ways, with the most common classifications based on physical activity limitations, disease progression, severity, and treatment strategies.The Functional Classification of Heart Failure divides patients into four categories based on physical activity limitation due to symptom burden.Class I: Patients in this class have cardiac disease but no physical activity limitations. Ordinary activities like walking, climbing stairs, or routine tasks do not cause...
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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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Pathophysiology of Heart Failure01:17

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Heart failure (HF) is a progressive syndrome involving ventricles that leads to inadequate cardiac output. It can be classified based on location and output or ejection fraction. Ejection fraction (EF) is an essential measurement in the diagnosis and surveillance of HF. Reduced EF corresponds to systolic heart failure (HFrEF). However, HF with preserved ejection fraction (HFpEF) is becoming increasingly prevalent. Also known as diastolic HF, this form of HF is related to aging. The...
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Related Experiment Video

Updated: Jan 17, 2026

Lumped-Parameter and Finite Element Modeling of Heart Failure with Preserved Ejection Fraction
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Artificial intelligence-enabled electrocardiogram model for predicting heart failure with preserved ejection

David Hong1, Sung-Hee Song2, Heayoung Shin1

  • 1Division of Cardiology, Department of Medicine, Heart Vascular Stroke Institute, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul 06351, Republic of Korea.

European Heart Journal. Digital Health
|September 23, 2025
PubMed
Summary

An artificial intelligence (AI) electrocardiogram (ECG) model can predict heart failure with preserved ejection fraction (HFpEF) and identify patients at higher risk for adverse cardiac events. This AI-ECG tool aids in simplifying HFpEF diagnosis and prognosis.

Keywords:
Artificial intelligenceElectrocardiogramHeart failure

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Area of Science:

  • Cardiology
  • Artificial Intelligence in Medicine
  • Medical Diagnostics

Background:

  • Diagnosing heart failure with preserved ejection fraction (HFpEF) is challenging due to the absence of a single definitive marker, necessitating multiple complex tests.
  • Current diagnostic pathways for HFpEF often involve echocardiography and biomarker measurements, which can be time-consuming and resource-intensive.

Purpose of the Study:

  • To develop and validate an artificial intelligence (AI)-enabled electrocardiogram (ECG) model for the prediction of HFpEF.
  • To assess the prognostic capability of the AI-ECG model in stratifying patients based on their risk of adverse cardiovascular outcomes.

Main Methods:

  • A retrospective cohort study utilizing data from 13,081 patients, classified using the HFA-PEFF score (HFpEF ≥5, control <5).
  • A convolutional neural network was trained on ECG data to predict HFpEF, with performance evaluated by AUROC.
  • Patients were divided into training, validation, and test sets in a 7:1:2 ratio.

Main Results:

  • The AI-ECG model achieved a good discriminative performance for HFpEF prediction with an AUROC of 0.81 (95% CI 0.79-0.82).
  • Consistent model performance was observed across subgroups stratified by HFpEF risk factors.
  • Positive AI-ECG classification was associated with significantly higher risks of cardiac death (HR 9.56) and heart failure hospitalization (HR 5.91) at 5 years.

Conclusions:

  • The AI-ECG model serves as a reliable tool for predicting HFpEF, as defined by the HFA-PEFF score.
  • The model effectively stratifies patients according to their prognosis, identifying those at higher risk for adverse events.
  • Clinical integration of this AI-ECG model could streamline the diagnostic process and improve patient management for HFpEF.