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Related Concept Videos

Disturbances in Heart Rhythm01:28

Disturbances in Heart Rhythm

936
Arrhythmia or dysrhythmia refers to an abnormal heart rhythm caused by a defect in the heart's conduction system. It can cause the heart to beat irregularly, too quickly, or too slowly, leading to symptoms like chest pain, shortness of breath, and fainting. Factors such as stress, caffeine, alcohol, nicotine, cocaine, certain drugs, congenital defects, diseases, and electrolyte abnormalities can trigger arrhythmias.
Arrhythmias are categorized by their speed, rhythm, and origin. A slow...
936
Pulse rhythm01:30

Pulse rhythm

785
Pulse rhythm refers to the pattern of pulsations within specific intervals, offering valuable insights into the regularity or irregularity of the heart's beats as observed through the pattern of pulsation within specific intervals. A regular pulse exhibits a consistent heart rate with uniform waveforms and pulsation force, variations of which can be classified as normal, weak, or bounding.
Conversely, an irregular pulse pattern is termed dysrhythmia, stemming from disruptions in cardiac...
785
Electrocardiogram01:29

Electrocardiogram

2.3K
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...
2.3K
Mechanism of Cardiac Arrhythmias01:28

Mechanism of Cardiac Arrhythmias

912
Arrhythmias are irregular heart rhythms occurring when the heart's electrical impulses become abnormal. These disturbances can lead to various symptoms, depending on their severity and the underlying cause. Some common factors contributing to arrhythmias include hypoxia, ischemia, electrolyte imbalances, excessive catecholamine exposure, drug toxicity, and muscle overstretching. Arrhythmias can be classified into two main types based on the rate and site of origin of abnormal heart rhythms.
912
Electrocardiogram Fundamentals01:28

Electrocardiogram Fundamentals

566
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...
566
ECG Interpretation of Arrhythmias I: Sinus Arrhythmias01:16

ECG Interpretation of Arrhythmias I: Sinus Arrhythmias

211
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,...
211

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Related Experiment Video

Updated: Jun 25, 2025

Real-Time Cardiac Mapping with a Noninvasive Imageless Electrocardiographic Imaging System
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Real-Time Cardiac Mapping with a Noninvasive Imageless Electrocardiographic Imaging System

Published on: April 11, 2025

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Artificial intelligence for ventricular arrhythmia capability using ambulatory electrocardiograms.

Joseph Barker1,2,3,4,5, Xin Li1,6, Ahmed Kotb1,2

  • 1Department of Cardiovascular Sciences, University of Leicester, Glenfield Hospital, Groby Road, Leicester LE3 9QP, UK.

European Heart Journal. Digital Health
|May 22, 2024
PubMed
Summary
This summary is machine-generated.

An artificial intelligence model, VA-ResNet-50, can predict ventricular arrhythmia (VA) risk from electrocardiograms (ECGs) with high accuracy. This AI tool shows promise for improving patient outcomes and guiding the use of implantable cardioverter defibrillators.

Keywords:
Artificial intelligenceDeep learningImplantable cardioverter defibrillatorNeural networkRisk stratificationVentricular arrhythmia

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Area of Science:

  • Cardiology
  • Artificial Intelligence
  • Medical Diagnostics

Background:

  • Current clinical guidelines for implantable cardioverter defibrillators (ICDs) have limitations in accurately stratifying ventricular arrhythmia (VA) risk.
  • This inaccuracy leads to significant patient morbidity and mortality.
  • Artificial intelligence (AI) presents a novel approach for VA risk stratification using electrocardiograms (ECGs).

Purpose of the Study:

  • To develop and validate a deep neural network (DNN) for VA risk stratification.
  • To assess the capability of AI to determine VA risk from routine ambulatory ECGs.

Main Methods:

  • A multicentre case-control study utilized an open-source ResNet-50-based DNN, VA-ResNet-50.
  • The model analyzed three-lead, 24-hour ambulatory ECGs to predict VA capability.
  • 270 adult patients (159 with VA) were included, with ECGs collected up to 1.6 years prior to VA events.

Main Results:

  • VA-ResNet-50 achieved an accuracy of 0.76 and an F1 score of 0.79 in classifying VA capability from ECGs.
  • The model demonstrated an area under the receiver operator curve of 0.8.
  • Individuals identified as high-risk by the AI had a 2.87 times higher relative risk of VA.

Conclusions:

  • Ambulatory ECGs contain valuable risk signals for VA stratification when analyzed by VA-ResNet-50.
  • The AI model's performance surpasses current medical guidelines.
  • This AI-driven approach holds promise for optimizing the allocation of life-saving ICDs.