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Related Concept Videos

Electrocardiogram01:29

Electrocardiogram

3.2K
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...
3.2K
Pulse rhythm01:30

Pulse rhythm

925
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...
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Holter Monitor: 24-Hour Monitoring01:23

Holter Monitor: 24-Hour Monitoring

269
Holter monitoring is a continuous electrocardiography (ECG) recording that tracks the heart's electrical activity over an extended period, generally 24 to 48 hours. This noninvasive diagnostic tool detects irregular heart rhythms that may not be captured during a standard ECG performed in a clinical setting.DeviceThe Holter monitor is a portable, small device connected to several electrodes on the patient's chest. These electrodes detect the heart's electrical signals and transmit them to the...
269

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Related Experiment Video

Updated: Sep 10, 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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Deep Learning Can Unmask Conduction Tissue Disease From an Ambulatory ECG.

Laurent Fiorina1,2, Tanner Carbonati3, Baptiste Maille4,5

  • 1Ramsay Santé, Institut Cardiovasculaire Paris Sud, Hôpital privé Jacques Cartier, Massy, France (L.F.).

Circulation. Arrhythmia and Electrophysiology
|August 26, 2025
PubMed
Summary

A novel deep learning model using 24-hour ECG effectively detects past bradyarrhythmia episodes, aiding in timely diagnosis of syncope causes. This advanced tool identifies conduction tissue disease for improved patient management.

Keywords:
artificial intelligencebradycardiadeep learningsick sinus syndromesyncope

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Patient Directed Recording of a Bipolar Three-Lead Electrocardiogram using a Smartwatch with ECG Function
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Area of Science:

  • Cardiology
  • Artificial Intelligence
  • Medical Diagnostics

Background:

  • Bradyarrhythmia is a frequent, serious cause of syncope, often intermittent and hard to detect.
  • Conventional ECG monitoring has limitations in accuracy and speed, increasing recurrence risks.

Purpose of the Study:

  • To develop and validate a deep learning model for detecting past bradyarrhythmia using 24-hour single-lead ECG.
  • To assess the model's ability to identify specific bradyarrhythmias like sinus pauses and heart block.

Main Methods:

  • A deep learning model was trained on 14-day ambulatory ECG recordings to identify prior bradyarrhythmias.
  • The model analyzed the last 24 hours of ECG data to detect sinus pauses (≥3s daytime, ≥6s anytime) and complete heart block.

Main Results:

  • External validation showed high accuracy (AUC 0.87-0.93) for detecting various bradyarrhythmias, with excellent negative predictive values (97.9-99.9%).
  • The model also predicted future bradyarrhythmia events with an AUC of 0.88 for the composite endpoint.

Conclusions:

  • Deep learning-enabled ambulatory ECG can reveal underlying conduction tissue disease.
  • This technology offers potential for earlier identification and management of intermittent bradyarrhythmia, improving patient outcomes.