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Related Concept Videos

Electrocardiogram01:29

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.
Three major waveforms are present in a typical ECG recording: the P wave, the QRS complex, and...
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ECG Interpretation of Arrhythmias II: Atrial, Junctional and Ventricular Arrhythmias01:25

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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ECG Interpretation of Arrhythmias I: Sinus Arrhythmias01:16

ECG Interpretation of Arrhythmias I: Sinus Arrhythmias

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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,...
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Correlation between ECG and Cardiac Cycle01:25

Correlation between ECG and Cardiac Cycle

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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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Pulse rhythm01:30

Pulse rhythm

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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...
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Dysrhythmias III: Characteristics of Dysrhythmias01:29

Dysrhythmias III: Characteristics of Dysrhythmias

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Dysrhythmias, also known as arrhythmias, are irregular heart rhythms that result from abnormal electrical activity in the heart, affecting its ability to circulate blood efficiently. Tachyarrhythmias, a subset of dysrhythmias, are characterized by abnormally fast heart rates exceeding 100 beats per minute. Here are some types of tachyarrhythmias with their distinct ECG features:Sinus Tachycardia:Sinus tachycardia presents a regular heart rhythm with an increased rate of 101-180 beats per...
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Real-Time Cardiac Mapping with a Noninvasive Imageless Electrocardiographic Imaging System
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An artificial intelligence-enabled ECG algorithm for identifying ventricular premature contraction during sinus

Sheng-Nan Chang1, Yu-Heng Tseng2, Jien-Jiun Chen1

  • 1Division of Cardiology, Department of Internal Medicine, National Taiwan University College of Medicine and Hospital Yun-Lin Branch, Dou-Liu City, Taiwan.

European Journal of Medical Research
|December 14, 2022
PubMed
Summary

Artificial intelligence (AI) can detect ventricular premature complexes (VPCs) using normal sinus rhythm (NSR) electrocardiograms (ECGs). This AI-enabled ECG approach offers rapid, point-of-care identification of VPCs, improving upon traditional long-term monitoring methods.

Keywords:
12-Lead electrocardiogramArtificial intelligenceConvolutional neural networkVentricular premature complex

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Area of Science:

  • Cardiology
  • Artificial Intelligence in Medicine
  • Medical Diagnostics

Background:

  • Ventricular premature complexes (VPCs) are common arrhythmias with potential to trigger serious cardiac events.
  • Current VPC screening methods are costly, time-consuming, and less effective for low-frequency VPCs.
  • Twelve-lead electrocardiograms (ECGs) are a low-cost, widely accessible diagnostic tool.

Purpose of the Study:

  • To develop and evaluate an AI-enabled ECG algorithm for identifying patients with VPCs during normal sinus rhythm (NSR).
  • To leverage machine learning for improved detection of VPCs, overcoming limitations of traditional monitoring.

Main Methods:

  • A convolutional neural network (CNN) algorithm was developed to detect VPC signatures in standard 12-lead ECGs during NSR.
  • ECG records from 398 patients with VPCs were analyzed, including 1617 NSR ECGs without VPCs and 753 normal ECGs for comparison.
  • Both image and time-series data formats were utilized for training and optimizing CNN models (InceptionV3, ResNet50V2).

Main Results:

  • The AI-enabled ECG models demonstrated satisfactory performance in predicting VPCs.
  • The single-input image model (InceptionV3) achieved an accuracy of 0.895 (95% CI 0.683-0.937).
  • The multi-input time-series model (ResNet50V2) achieved an accuracy of 0.880 (95% CI 0.646-0.943), outperforming the single-input time-series model (0.840).

Conclusions:

  • AI-enabled ECG analysis during NSR can rapidly identify individuals with VPCs at the point of care.
  • This technology holds potential for automatic prediction of VPC episodes, reducing reliance on prolonged monitoring.
  • AI offers a promising, efficient alternative for detecting and managing VPCs in clinical practice.