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Correlation between ECG and Cardiac Cycle

Correlation between ECG and Cardiac Cycle

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...

Electrocardiogram Fundamentals

Electrocardiogram Fundamentals

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...

ECG Interpretation of Rhythms

ECG Interpretation of Rhythms

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....

Electrocardiogram

Electrocardiogram

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...

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

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

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...

Cardiac Action Potential

Cardiac Action Potential

Cardiac action potentials are essential for proper heart function, enabling the rhythmic contractions needed for adequate blood circulation. Nodal cells and Purkinje fibers, specialized for electrical conduction, generate these action potentials.
The cardiac action potential process involves a series of phases characterized by the movement of ions across the cardiac cell membranes, leading to the depolarization and repolarization of the cardiac myocytes.
Ionic Basis of Cardiac Action Potentials

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Related Experiment Video

Updated: Mar 17, 2026

Analyzing Long-Term Electrocardiography Recordings to Detect Arrhythmias in Mice

Analyzing Long-Term Electrocardiography Recordings to Detect Arrhythmias in Mice

Published on: May 23, 2021

Using ordinal partition transition networks to analyze ECG data.

Christopher W Kulp¹, Jeremy M Chobot¹, Helena R Freitas¹

¹The Department of Astronomy and Physics, Lycoming College, Williamsport, Pennsylvania 17701, USA.

Chaos (Woodbury, N.Y.)

|August 1, 2016

Summary

Ordinal pattern partition networks reveal significant differences in electrocardiogram (ECG) mean degrees between healthy individuals and patients with various heart conditions, aiding in cardiac diagnostics.

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Area of Science:

Cardiology
Network Science
Time Series Analysis

Background:

Electrocardiogram (ECG) analysis is crucial for diagnosing heart conditions.
Traditional ECG interpretation can be complex and time-consuming.
Novel network-based approaches offer potential for enhanced diagnostic capabilities.

Purpose of the Study:

To investigate the utility of ordinal pattern partition networks for analyzing ECG data.
To identify network-derived features that differentiate between healthy and diseased heart conditions.
To compare the diagnostic power of network mean degree, entropy, and non-occurring patterns (NFP).

Main Methods:

ECG time series data from patients with various heart conditions were symbolized into ordinal patterns.
Ordinal pattern partition networks were constructed based on the temporal sequence of these patterns.
Network measures including mean degree, entropy, and NFP were computed for each ECG series.

Main Results:

A statistically significant difference was observed in the distribution of mean degrees between healthy and unhealthy patient groups.
The mean degree measure effectively distinguished between healthy individuals and several specific heart conditions.
Distributions of entropy and NFP did not show statistically significant differences across the studied groups.

Conclusions:

Ordinal pattern partition network mean degree is a promising biomarker for differentiating cardiac health status.
This network-based approach provides a novel method for ECG data analysis in cardiology.
Further research can explore the clinical application of mean degree in diagnosing and monitoring heart conditions.