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Related Concept Videos

Electrocardiogram01:29

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 the T...
Electrocardiogram Fundamentals01:28

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...
Correlation between ECG and Cardiac Cycle01:25

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...
Instrumentation Amplifier01:25

Instrumentation Amplifier

An electrocardiography (ECG) machine is an essential piece of medical equipment used to monitor the electrical activity of the heart. It operates by detecting small electrical changes on the skin that result from the depolarization of the heart muscle during each heartbeat. However, these signals are in the microvolt range and can be easily overwhelmed by noise or interference.
To overcome this challenge, an ECG machine utilizes an instrumentation amplifier. This specialized amplifier is...
ECG Interpretation of Rhythms01:24

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. When...

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Related Experiment Video

Updated: May 9, 2026

BrainBeats as an Open-Source EEGLAB Plugin to Jointly Analyze EEG and Cardiovascular Signals
08:22

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

Published on: April 26, 2024

Extracting Genetically-Imputed Causal Features From ECG Data.

Yuchen Yao1,2, Zhaotong Lin3, Xiaotong Shen1

  • 1School of Statistics, University of Minnesota, Minneapolis, Minnesota, USA.

Statistical Analysis and Data Mining
|May 8, 2026
PubMed
Summary
This summary is machine-generated.

Genetic factors in electrocardiograms (ECGs) significantly contribute to atrial fibrillation (AF) development. New methods like DeepFEIVR analyze ECG data to uncover these causal links, improving understanding of AF etiology.

Keywords:
GWASatrial fibrillationcausal inferencedeep learninginstrumental variable regression

Related Experiment Videos

Last Updated: May 9, 2026

BrainBeats as an Open-Source EEGLAB Plugin to Jointly Analyze EEG and Cardiovascular Signals
08:22

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

Published on: April 26, 2024

Area of Science:

  • Cardiology
  • Genetics
  • Artificial Intelligence

Background:

  • Atrial fibrillation (AF) is a common arrhythmia linked to stroke, heart failure, and mortality.
  • Electrocardiograms (ECGs) are crucial for AF diagnosis, recording heart's electrical activity.
  • Deep learning and Mendelian randomization (MR) are emerging tools for AF prediction and causal inference.

Purpose of the Study:

  • To apply and extend the DeepFEIVR method for identifying genetically imputed causal ECG features associated with AF.
  • To investigate the causal role of ECG genetic components in AF development using the UK Biobank dataset.
  • To enhance DeepFEIVR and DeepFEIVR-RI for handling numerous instrumental variables (IVs) and to visualize extracted causal features.

Main Methods:

  • Application of DeepFEIVR and DeepFEIVR-RI (a variant with residual inclusion) to the UK Biobank dataset.
  • Extension of DeepFEIVR and DeepFEIVR-RI to accommodate a large number of IVs.
  • Utilized dnn-loc algorithm for visual examination of extracted ECG causal features.

Main Results:

  • Statistically significant evidence (p < 10^-8) that genetic components in ECGs contribute to AF development.
  • Demonstrated the efficacy of DeepFEIVR and DeepFEIVR-RI in identifying disease-associated causal features from high-dimensional data.
  • Provided a comparative analysis of DeepFEIVR and DeepFEIVR-RI using various IVs.

Conclusions:

  • Genetic factors influencing ECG characteristics play a significant causal role in the etiology of atrial fibrillation.
  • The extended DeepFEIVR framework effectively identifies genetic ECG components associated with AF, advancing causal inference in cardiovascular research.
  • Visualizing extracted causal features aids in understanding the underlying mechanisms of AF.