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Related Concept Videos

Electrocardiogram Fundamentals01:28

Electrocardiogram Fundamentals

471
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...
471
Instrumentation Amplifier01:25

Instrumentation Amplifier

417
An electrocardiography (ECG) machine is an essential piece of medical equipment used to monitor the electrical activity of the heart. It operates by detecting small electrical changes on the skin that result from the depolarization of the heart muscle during each heartbeat. However, these signals are in the microvolt range and can be easily overwhelmed by noise or interference.
To overcome this challenge, an ECG machine utilizes an instrumentation amplifier. This specialized amplifier is...
417
ECG Interpretation of Rhythms01:24

ECG Interpretation of Rhythms

389
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....
389
Electrocardiogram01:29

Electrocardiogram

2.0K
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...
2.0K

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Related Experiment Video

Updated: May 22, 2025

Patient-specific Modeling of the Heart: Estimation of Ventricular Fiber Orientations
12:09

Patient-specific Modeling of the Heart: Estimation of Ventricular Fiber Orientations

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Foundation models for generalizable electrocardiogram interpretation: comparison of supervised and self-supervised

Alexis Nolin-Lapalme1,2,3,4, Achille Sowa1,2,4, Jacques Delfrate2,4

  • 1Department of Biochemistry and Molecular Medicine, Faculty of Medicine, University of Montreal, Montreal, Quebec, Canada.

Medrxiv : the Preprint Server for Health Sciences
|March 17, 2025
PubMed
Summary

This study introduces two open-source artificial intelligence (AI) models for electrocardiogram (ECG) interpretation, demonstrating that self-supervised learning (SSL) enhances generalizability and performance, especially with limited data. The findings support SSL as a key method for accessible and fair AI-driven cardiac diagnostics.

Keywords:
Artificial intelligenceElectrocardiogramFairnessFoundation modelGeneralizabilityPrivacy

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Area of Science:

  • Artificial Intelligence in Medicine
  • Cardiovascular Diagnostics
  • Machine Learning for Healthcare

Background:

  • Existing artificial intelligence (AI) solutions for 12-lead electrocardiogram (ECG) interpretation often lack generalizability and are closed-source.
  • Supervised learning (SL) limitations hinder adaptability of AI ECG interpretation across diverse clinical settings.
  • Open-source foundational models are needed to address challenges in current AI ECG diagnostics.

Purpose of the Study:

  • To develop and compare two open-source foundational ECG models: DeepECG-SSL (self-supervised learning) and DeepECG-SL (supervised learning).
  • To evaluate the generalizability, fairness, and performance of these models across diverse clinical datasets.
  • To establish self-supervised learning as a viable paradigm for ECG analysis, particularly in data-limited scenarios.

Main Methods:

  • Trained two models (DeepECG-SSL and DeepECG-SL) on over 1 million ECGs with standardized preprocessing and automated report analysis.
  • Pretrained DeepECG-SSL using self-supervised contrastive learning and masked lead modeling.
  • Evaluated models on six multilingual private healthcare systems and four public datasets, assessing performance across 77 cardiac conditions and conducting fairness analyses for age and sex.

Main Results:

  • Both models achieved high Area Under the Receiver Operating Characteristic Curve (AUROC) scores across internal, external public, and external private datasets (e.g., DeepECG-SSL internal AUROC: 0.990).
  • Fairness analyses revealed minimal performance disparities across age and sex groups (true positive rate & false positive rate difference < 0.010).
  • DeepECG-SSL demonstrated superior performance in digital biomarker prediction with limited labeled data, including 5-year atrial fibrillation risk (AUROC 0.742 vs. 0.720) and Long QT syndrome classification (AUROC 0.931 vs. 0.853).

Conclusions:

  • Open-sourcing model weights, preprocessing tools, and validation code supports robust, data-efficient AI diagnostics.
  • Self-supervised learning (SSL) is a promising paradigm for ECG analysis, enhancing accessibility, generalizability, and fairness.
  • This work facilitates broader adoption of AI in cardiac diagnostics, especially in resource-limited clinical environments.