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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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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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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...
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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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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: Nov 23, 2025

Analyzing Long-Term Electrocardiography Recordings to Detect Arrhythmias in Mice
06:07

Analyzing Long-Term Electrocardiography Recordings to Detect Arrhythmias in Mice

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Multiplex recurrence networks from multi-lead ECG data.

Sneha Kachhara1, G Ambika1

  • 1Indian Institute of Science Education and Research (IISER) Tirupati, Tirupati 517507, India.

Chaos (Woodbury, N.Y.)
|December 31, 2020
PubMed
Summary
This summary is machine-generated.

Multiplex recurrence networks (MRNs) reveal distinct patterns in electrocardiogram (ECG) data between healthy individuals and those with heart conditions. This approach offers a novel way to analyze complex cardiovascular dynamics for disease detection.

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Area of Science:

  • Cardiovascular Physiology
  • Complex Systems Analysis
  • Network Science

Background:

  • Electrocardiogram (ECG) data provides insights into cardiac electrical activity.
  • Analyzing spatio-temporal dynamics of the heart is crucial for understanding cardiovascular health and disease.
  • Traditional methods may not fully capture the complexity of multi-lead ECG signals.

Purpose of the Study:

  • To introduce an integrated framework using multiplex recurrence networks (MRNs) for analyzing multi-lead ECG data.
  • To investigate how intralayer and interlayer topological features of MRNs reflect cardiac system dynamics.
  • To differentiate between healthy and diseased cardiac states using MRN analysis.

Main Methods:

  • Construction of multiplex recurrence networks (MRNs) from multi-lead ECG data.
  • Analysis of intralayer and interlayer topological features, including mutual information and degree distributions.
  • Comparison of MRN characteristics between healthy subjects and patients with cardiac abnormalities.

Main Results:

  • MRNs from healthy ECG data exhibit higher coherence and mutual information, with less divergence in degree distributions.
  • Significant differences in inter-layer similarity measures are observed in diseased states.
  • Localized abnormalities, like bundle branch block, most notably affect MRN coherence.

Conclusions:

  • Multiplex recurrence networks provide a sensitive method for detecting variations in cardiac dynamics.
  • Comprehensive analysis of MRN measures is essential for identifying disease-specific patterns.
  • The MRN framework is broadly applicable to multivariate or multi-channel data from complex systems.