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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...

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 Arrhythmias I: Sinus Arrhythmias

ECG Interpretation of Arrhythmias I: Sinus Arrhythmias

Arrhythmias are disturbances in the heart's rhythm that lead to abnormal heartbeats. These irregularities can originate from different parts of the heart and are classified based on their origin and nature.
Types of Arrhythmias
Sinus Node Arrhythmias
Sinus Bradycardia: Originating from the sinoatrial (SA) node, sinus bradycardia involves slower impulses, resulting in a heart rate of less than 60 beats per minute (bpm). Causes include sleep, vagal stimulation, beta-blockers, hypothyroidism,...

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

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...

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Related Experiment Video

Updated: Jun 26, 2025

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

Predicting and Recognizing Drug-Induced Type I Brugada Pattern Using ECG-Based Deep Learning.

Paul-Adrian Călburean^1,2, Luigi Pannone¹, Cinzia Monaco¹

¹Heart Rhythm Management Centre, Postgraduate Program in Cardiac Electrophysiology and Pacing Universitair Ziekenhuis Brussel, Vrije Universiteit Brussel, European Reference Networks Guard-Heart Brussels Belgium.

Journal of the American Heart Association

|May 10, 2024

Summary

This summary is machine-generated.

A deep convolutional neural network, BrS-Net, accurately identifies Brugada syndrome (BrS) type I patterns. BrS-Net also predicts BrS development from baseline ECGs, aiding in sudden cardiac death prevention.

Keywords:

Brugada syndrome ajmaline testing artificial intelligence deep learning

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Last Updated: Jun 26, 2025

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Published on: April 11, 2025

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Published on: December 11, 2019

Area of Science:

Cardiology
Artificial Intelligence
Medical Diagnostics

Background:

Brugada syndrome (BrS) is linked to sudden cardiac death in healthy individuals.
Drug-induced BrS constitutes a significant portion of all BrS cases (55-70%).

Purpose of the Study:

To develop and evaluate a deep convolutional neural network (CNN) for Brugada syndrome diagnosis.
To assess the CNN's ability to recognize and predict BrS type I patterns.

Main Methods:

Wavelet analysis transformed ECG tracings from baseline and ajmaline infusion.
A CNN, termed BrS-Net, was trained to recognize and predict BrS type I patterns.
The study included 1188 patients undergoing ajmaline testing.

Main Results:

BrS-Net achieved high performance in recognizing BrS type I patterns during ajmaline (AUC-ROC 0.945, AUC-PR 0.892).
BrS-Net demonstrated good predictive performance for BrS type I development from baseline ECGs (AUC-ROC 0.805, AUC-PR 0.605).
30.3% of patients developed a BrS type I pattern during ajmaline infusion.

Conclusions:

BrS-Net effectively identifies BrS type I patterns with high accuracy.
BrS-Net shows promise in predicting BrS development from baseline ECGs in an unselected population.