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Related Concept Videos

Electrocardiogram01:29

Electrocardiogram

2.2K
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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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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Electrocardiogram Fundamentals01:28

Electrocardiogram Fundamentals

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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...
543
Disturbances in Heart Rhythm01:28

Disturbances in Heart Rhythm

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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...
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Mechanism of Cardiac Arrhythmias01:28

Mechanism of Cardiac Arrhythmias

899
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.
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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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Patient Directed Recording of a Bipolar Three-Lead Electrocardiogram using a Smartwatch with ECG Function
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Artificial intelligence-based electrocardiogram analysis improves atrial arrhythmia detection from a smartwatch

Laurent Fiorina1, Pascale Chemaly1, Joffrey Cellier1

  • 1Ramsay Santé, Institut Cardiovasculaire Paris Sud, Hôpital privé Jacques Cartier, 6 avenue du Noyer Lambert, 91 300 Massy, France.

European Heart Journal. Digital Health
|September 25, 2024
PubMed
Summary

Deep neural networks (DNNs) significantly improve smartwatch ECG accuracy for detecting atrial arrhythmias (AAs), outperforming standard smartwatch software in clinical cardiology settings.

Keywords:
Artificial intelligenceAtrial fibrillationDeep learningSmartwatchWearable

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Area of Science:

  • Cardiology
  • Biomedical Engineering
  • Artificial Intelligence in Medicine

Background:

  • Smartwatch ECGs (SW ECGs) offer non-invasive assessment of abnormal heart rhythms like atrial arrhythmias (AAs), which are linked to stroke risk.
  • Current SW ECG performance is limited, necessitating advanced algorithms like deep neural networks (DNNs) for improved accuracy, especially in diverse clinical populations.

Purpose of the Study:

  • To evaluate the diagnostic performance of a DNN algorithm applied to SW ECGs for detecting atrial arrhythmias in a clinical cardiology patient cohort.
  • To compare the DNN algorithm's accuracy against the standard Apple watch ECG software and expert 12-lead ECG interpretation.

Main Methods:

  • Conducted two clinical trials with 400 patients, recording simultaneous SW ECGs and 12-lead ECGs (12L ECGs).
  • Processed SW ECGs using a DNN algorithm and the Apple watch ECG software.
  • Compared SW ECG interpretations against expert electrophysiologist adjudication of 12L ECGs, reporting sensitivity, specificity, and inconclusive rates.

Main Results:

  • The DNN algorithm achieved 91% sensitivity and 95% specificity, significantly outperforming the Apple app (61% sensitivity, 97% specificity) versus expert interpretation.
  • The DNN provided a diagnosis for 99% of ECGs, while the Apple app had 22% inconclusive results.
  • The DNN demonstrated superior accuracy and diagnostic coverage compared to the standard SW ECG software.

Conclusions:

  • A DNN-based algorithm applied to SW ECGs provides highly accurate atrial arrhythmia detection in a clinical cardiology setting.
  • DNNs significantly enhance the diagnostic utility of SW ECGs, offering a more reliable tool for identifying arrhythmias compared to existing smartwatch software.