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Electrocardiogram01:29

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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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Electrocardiogram Fundamentals01:28

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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
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Stress triggers a coordinated physiological response involving the sympathetic nervous system (SNS) and the hypothalamic-pituitary-adrenal (HPA) axis. This dual activation ensures that the body is prepared for both immediate and prolonged stress management. The process begins with the perception of a stressor. This initial phase activates the SNS, leading to the rapid release of adrenaline (epinephrine) from the adrenal glands.
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According to George Herbert Mead, as children progress beyond the game stage, they develop a more comprehensive understanding of societal rules and norms. This cognitive and social development enables them to internalize the expectations of the broader community, refining their ability to regulate behavior.Consistent participation in organized activities is crucial in helping children recognize that their actions are not isolated but contribute to a more significant, interconnected group...
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Theories play an essential role in organizing patient care. Theories refer to a proposed or followed belief, policy, or procedure that is the basis for action. Nursing theories are knowledge-based concepts that guide nurses' actions, influence nursing education and practice, and allow nurses to care for their patients.
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The development of self in children is deeply rooted in social interactions, mainly through stages of play and structured games. These stages, outlined by sociologist George Herbert Mead, illustrate how children progressively learn to understand and adopt social roles, forming a cohesive sense of self.The Play Stage: Imitation and Simple Role-TakingIn the early years of childhood, the play stage is characterized by imitative behavior, where children engage in role-playing based on familiar...
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Modelos fundacionales para la interpretación de electrocardiogramas: implicaciones clínicas

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 H3C 3J7.

European heart journal
|January 22, 2026
PubMed
Resumen

Este estudio presenta dos modelos de inteligencia artificial (IA) de código abierto para la interpretación de electrocardiogramas (ECG). El aprendizaje autosupervisado (SSL) muestra ser prometedor para mejorar el diagnóstico por IA en la atención cardíaca, especialmente con datos limitados.

Palabras clave:
Inteligencia artificialCLSAElectrocardiogramaEquidadModelo fundacionalGeneralizaciónPrivacidadUK Biobank

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Área de la Ciencia:

  • Inteligencia artificial en medicina
  • Diagnóstico cardiovascular
  • Aprendizaje automático para la atención médica

Sus antecedentes:

  • La IA existente para la interpretación de ECG a menudo carece de generalización y se basa en el aprendizaje supervisado (SL) con datos etiquetados extensos.
  • El aprendizaje autosupervisado (SSL) ofrece una solución potencial al aprender de datos no etiquetados, superando las limitaciones de los métodos tradicionales.
  • Este estudio aborda la necesidad de soluciones de IA adaptables y de código abierto en el diagnóstico cardíaco.

Objetivo del estudio:

  • Desarrollar y comparar dos modelos fundacionales de ECG de código abierto: DeepECG-SL (supervisado) y DeepECG-SSL (autosupervisado).
  • Evaluar la generalización, equidad y rendimiento de SSL frente a SL en la interpretación de ECG en diversos entornos clínicos.
  • Proporcionar herramientas de IA accesibles para diagnósticos cardíacos robustos y eficientes en datos.

Principales métodos:

  • Se entrenaron dos modelos, DeepECG-SL y DeepECG-SSL, con más de 1 millón de ECGs para predecir 77 condiciones cardíacas.
  • DeepECG-SSL utilizó aprendizaje contrastivo y modelado de derivaciones enmascaradas en datos no etiquetados antes del ajuste fino.
  • Se evaluó el rendimiento en siete sistemas de salud privados y cuatro públicos multilingües, incluidas evaluaciones de equidad y privacidad.

Principales resultados:

  • Ambos modelos lograron un alto rendimiento (AUROC ~0.98-0.99) en conjuntos de datos internos, públicos y externos privados.
  • DeepECG-SSL mostró un rendimiento superior en tareas con datos limitados, como la clasificación genotípica del síndrome de QT largo y la predicción del riesgo de fibrilación auricular.
  • Los análisis de equidad indicaron disparidades mínimas entre los grupos de edad y sexo para ambos modelos.

Conclusiones:

  • El aprendizaje autosupervisado (SSL) es un paradigma prometedor para el análisis de ECG, que mejora la accesibilidad, la generalización y la equidad en el diagnóstico cardíaco impulsado por IA.
  • La publicación de código abierto de modelos, herramientas y código tiene como objetivo fomentar diagnósticos de IA robustos y eficientes en datos.
  • Los modelos SSL son particularmente beneficiosos en entornos clínicos con datos anotados limitados.