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Related Concept Videos

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...
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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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ECG Interpretation of Rhythms01:24

ECG Interpretation of Rhythms

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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....
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Dysrhythmias V: Evaluating Dysrhythmias01:30

Dysrhythmias V: Evaluating Dysrhythmias

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Dysrhythmias, also known as arrhythmias, are disturbances in the heart's rhythm that range from benign to life-threatening. A thorough evaluation is crucial for appropriate management and involves a comprehensive medical history, physical examination, and various diagnostic tests.Medical HistorySymptoms: Collect detailed information on palpitations, dizziness, syncope, chest pain, and fatigue. Note their onset, frequency, and triggers.Previous Cardiac Issues: Document any history of heart...
312
ECG Interpretation of Arrhythmias II: Atrial, Junctional and Ventricular Arrhythmias01:25

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

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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...
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Related Experiment Video

Updated: Jan 9, 2026

Analyzing Long-Term Electrocardiography Recordings to Detect Arrhythmias in Mice
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Analyzing Long-Term Electrocardiography Recordings to Detect Arrhythmias in Mice

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Text-to-ECG: a Framework to Generate 12-Lead ECG from Text Reports.

Fabrizio Cattozzo, Sara Battiston, Massimo W Rivolta

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |December 3, 2025
    PubMed
    Summary
    This summary is machine-generated.

    A new Text-to-ECG (T2ECG) framework generates synthetic electrocardiogram (ECG) beats from text descriptions using Generative AI. This approach aids AI training data augmentation and simplifies ECG creation for non-experts.

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    Estimate the Cognitive Load Using Electrocardiographic Measure: A Human-AI Collaborative Task
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    Area of Science:

    • Artificial Intelligence in Medicine
    • Biomedical Signal Processing
    • Machine Learning for Healthcare

    Background:

    • Generative Artificial Intelligence (GenAI) is used to create synthetic electrocardiograms (ECGs) for augmenting training datasets, particularly for minority classes in AI decision support systems.
    • Existing methods often require specialized expertise for synthetic ECG generation.

    Purpose of the Study:

    • To introduce a novel Text-to-ECG (T2ECG) framework capable of generating synthetic 12-lead ECG heartbeats directly from textual diagnostic descriptions.
    • To evaluate the quality and realism of ECGs generated by the T2ECG framework for various cardiac conditions.

    Main Methods:

    • The T2ECG framework utilizes Bio_ClinicalBERT to create text embeddings and a Wasserstein Generative Adversarial Network with gradient penalty to synthesize ECG beats.
    • Training was conducted on the PTB-XL dataset, focusing on five diagnostic classes: normal sinus rhythm, inferior myocardial infarction (IMI), antero-septal myocardial infarction (ASMI), left anterior fascicular block (LAFB), and left ventricular hypertrophy (LVH).
    • Signal realism was assessed using visual inspection and quantitative analyses.

    Main Results:

    • The T2ECG framework successfully generated high-quality synthetic ECG heartbeats for most diagnostic classes.
    • The generated signals for antero-septal myocardial infarction (ASMI) were found to be of insufficient quality.
    • The framework demonstrated potential for data augmentation and simplified ECG generation via a text-based interface.

    Conclusions:

    • The T2ECG framework offers a viable method for generating synthetic ECGs from text, supporting AI model training and potentially democratizing ECG data creation.
    • While effective for several conditions, further refinement is needed for specific classes like ASMI.
    • This text-to-signal approach streamlines the process, making AI-driven ECG generation more accessible.