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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.
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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ECG Interpretation of Arrhythmias II: Atrial, Junctional and Ventricular Arrhythmias01:25

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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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Correlation between ECG and Cardiac Cycle01:25

Correlation between ECG and Cardiac Cycle

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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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Related Experiment Video

Updated: Apr 11, 2026

Analyzing Long-Term Electrocardiography Recordings to Detect Arrhythmias in Mice
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Differentiating long QT syndrome genotypes using electrocardiographic geometric parameterization and machine learning

Martina Srutova1, Lenka Lhotska1,2, Vaclav Kremen1,2

  • 1Department of Natural Sciences, Faculty of Biomedical Engineering, Czech Technical University in Prague, Kladno, Czech Republic.

Biomedical Physics & Engineering Express
|April 10, 2026
PubMed
Summary

This study automatically differentiates Long QT Syndrome (LQTS) genotypes using ECG data. This enables personalized management for preventing sudden cardiac death and developing wearable cardiac monitoring tools.

Keywords:
LQTS discriminationcardiovascular diseaseselectrocardiogram classificationelectrocardiogram parameterizationlong QT syndromesupport vector machine classification

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Area of Science:

  • Cardiology
  • Medical Informatics
  • Biomedical Engineering

Background:

  • Long QT Syndrome (LQTS) is an inherited cardiac disorder linked to ion channel dysfunction, causing prolonged QT intervals and increasing risks of syncope, arrhythmias, and sudden cardiac death.
  • Genotype-specific management is crucial for mitigating life-threatening arrhythmias in LQTS patients.
  • Accurate differentiation of LQTS genotypes (LQT1, LQT2, LQT3) is essential for targeted treatment strategies.

Purpose of the Study:

  • To develop an automated method for discriminating between LQT1, LQT2, and LQT3 genotypes using electrocardiogram (ECG) data.
  • To enable genotype-specific management and prevention strategies for Long QT Syndrome.
  • To explore the potential for noninvasive, portable diagnostic tools for LQTS.

Main Methods:

  • Utilized ECG data from the Telemetric and Holter ECG Warehouse's LQTS database.
  • Employed automated extraction of short ECG signals and geometric parameterization.
  • Classified genotypes using a two-stage cascade of binary support vector machine classifiers on Lead I ECG signals (200 Hz sampling).

Main Results:

  • Achieved 71% weighted accuracy on out-of-sample data for genotype discrimination.
  • Specific performance metrics: LQT1 (65% recall, 58% precision), LQT2 (79% recall, 82% precision), LQT3 (71% recall, 77% precision).
  • Demonstrated feasibility of noninvasive LQTS genotype differentiation through ECG morphological analysis.

Conclusions:

  • Noninvasive genotype differentiation for LQTS is feasible using ECG signal morphology.
  • This approach advances personalized cardiology and the development of portable diagnostic tools, like smartwatches.
  • Automated ECG analysis can support targeted treatment and prevention strategies for Long QT Syndrome.