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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
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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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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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Electrophysiology of Normal Cardiac Rhythm01:19

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The normal cardiac rhythm is a synchronized electrical activity that facilitates the regular and coordinated contraction of the heart muscle. This process is essential for efficient blood circulation throughout the body. The fundamental elements involved in establishing and maintaining this rhythm include the unique electrical properties of cardiac muscle cells, the sinoatrial (SA) node's pacemaker function, the specialized conducting system, and the ionic mechanisms underlying each phase...
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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: Dec 16, 2025

Patient-specific Modeling of the Heart: Estimation of Ventricular Fiber Orientations
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An Uncertainty Modeling Framework for Intracardiac Electrogram Analysis.

Amirhossein Koneshloo1, Dongping Du1, Yuncheng Du2

  • 1Department of Industrial, Manufacturing and Systems Engineering, Texas Tech University, Lubbock, TX 79409, USA.

Bioengineering (Basel, Switzerland)
|July 2, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces a statistical method to analyze heart electrical signals (intracardiac electrograms) despite data uncertainty. The approach reliably identifies abnormal electrical impulse sources, improving arrhythmia diagnosis.

Keywords:
intracardiac electrogram analysismaximum likelihood estimationstatistical modelinguncertainty analysis

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Area of Science:

  • Cardiovascular physiology
  • Biomedical engineering
  • Statistical modeling

Background:

  • Intracardiac electrograms (EGMs) are crucial for diagnosing cardiac arrhythmias and guiding catheter ablation.
  • Analyzing EGMs is challenging due to inherent data uncertainty and irregular signal variations.
  • Accurate localization of abnormal electrical sources is essential for effective treatment.

Purpose of the Study:

  • To develop a statistical approach for analyzing intracardiac electrograms (EGMs) that accounts for data uncertainty.
  • To accurately identify abnormal electrical impulse sources, even with complex wave phenomena.
  • To provide a reliable method for locating focal sources in cardiac arrhythmias.

Main Methods:

  • Modeled catheter sensor activation order using multinomial distribution.
  • Employed maximum likelihood estimations to track electrical wave conduction paths.
  • Utilized robust optimization based on local conduction velocity and geodesic distances.
  • Developed a statistical framework to handle data uncertainties in EGM analysis.

Main Results:

  • Successfully identified focal sources initiated by irregular electrical impulses.
  • Demonstrated capability to analyze complex wave dynamics like collisions, breakups, and spiral waves.
  • Provided reliable estimation of focal source locations despite significant data uncertainty.
  • Validated the efficacy of the statistical modeling framework for EGM analysis.

Conclusions:

  • The proposed statistical approach effectively addresses data uncertainty in EGM analysis.
  • This method offers a reliable way to pinpoint abnormal electrical impulse origins in the heart.
  • Statistical modeling shows significant potential for quantitative analysis of cardiac electrical activity.
  • Future work should integrate statistical methods with geometry-based approaches for enhanced diagnostics.