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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 to...

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.
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The horizontal axis measures time and rate, and the vertical axis measures amplitude or voltage. When...

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.
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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: Jun 25, 2026

BrainBeats as an Open-Source EEGLAB Plugin to Jointly Analyze EEG and Cardiovascular Signals

BrainBeats as an Open-Source EEGLAB Plugin to Jointly Analyze EEG and Cardiovascular Signals

Published on: April 26, 2024

A model-based Bayesian framework for ECG beat segmentation.

O Sayadi¹, M B Shamsollahi

¹Biomedical Signal and Image Processing Laboratory (BiSIPL), School of Electrical Engineering, Sharif University of Technology, Tehran, Iran. osayadi@ee.sharif.edu

Physiological Measurement

|February 27, 2009

Summary

This study demonstrates a Bayesian filtering method for effective electrocardiogram (ECG) beat segmentation and fiducial point extraction. The approach enhances clinical ECG analysis by accurately identifying key heart signal features.

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Last Updated: Jun 25, 2026

BrainBeats as an Open-Source EEGLAB Plugin to Jointly Analyze EEG and Cardiovascular Signals

BrainBeats as an Open-Source EEGLAB Plugin to Jointly Analyze EEG and Cardiovascular Signals

Published on: April 26, 2024

Area of Science:

Biomedical Engineering
Cardiology
Signal Processing

Background:

Electrocardiogram (ECG) analysis is crucial for understanding heart functionality.
ECG signal denoising and compression have been explored using Bayesian filtering.
Accurate ECG beat segmentation and fiducial point extraction are vital for diagnosis.

Purpose of the Study:

To investigate the application of a Bayesian filtering paradigm for ECG beat segmentation and fiducial point extraction.
To derive and evaluate analytic expressions for determining ECG points and intervals using this framework.

Main Methods:

Utilized a Bayesian filtering framework for ECG signal processing.
Developed analytic expressions for fiducial point and interval determination.
Evaluated the method on diverse real-world ECG signals.

Main Results:

The Bayesian filtering framework proved effective for ECG beat segmentation.
Analytic expressions enabled accurate determination of fiducial points and intervals.
Simulation results indicated enhanced clinical ECG beat segmentation performance.

Conclusions:

The proposed Bayesian filtering method offers a robust approach for ECG beat segmentation.
This technique can significantly improve the accuracy and efficiency of clinical ECG analysis.
The framework provides a valuable tool for extracting diagnostic features from ECG signals.