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Related Concept Videos

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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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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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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Holter Monitor: 24-Hour Monitoring01:23

Holter Monitor: 24-Hour Monitoring

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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...
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Related Experiment Video

Updated: Nov 9, 2025

A Research Method For Detecting Transient Myocardial Ischemia In Patients With Suspected Acute Coronary Syndrome Using Continuous ST-segment Analysis
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Reliable Deep Learning-Based Detection of Misplaced Chest Electrodes During Electrocardiogram Recording: Algorithm

Khaled Rjoob1, Raymond Bond1, Dewar Finlay1

  • 1Faculty of Computing, Engineering & Built Environment, Ulster University, Jordanstown, United Kingdom.

JMIR Medical Informatics
|April 16, 2021
PubMed
Summary
This summary is machine-generated.

Deep learning algorithms accurately detect precordial electrode misplacement on electrocardiograms (ECGs), outperforming experienced physicians. This technology can flag flawed ECG data, reducing misdiagnoses in cardiovascular disease detection.

Keywords:
ECGECG interpretationcardiovascular diseasedeep learningelectrode misplacementengineeringfeature engineeringmachine learningmyocardialmyocardial infarctionphysicians

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Area of Science:

  • Cardiology
  • Biomedical Engineering
  • Artificial Intelligence

Background:

  • The 12-lead electrocardiogram (ECG) is crucial for diagnosing cardiovascular diseases.
  • Electrode misplacement, particularly chest electrodes, can lead to misinterpretations and diagnostic errors.

Purpose of the Study:

  • To develop advanced algorithms for the automated detection of precordial (chest) electrode misplacement.
  • To improve the accuracy and reliability of ECG interpretations.

Main Methods:

  • Utilized traditional machine learning (ML) and deep learning (DL) models.
  • Trained algorithms on high-resolution body surface potential maps from patients with myocardial infarction, left ventricular hypertrophy, and normal ECGs.
  • Focused on detecting misplacement of V1 and V2 electrodes.

Main Results:

  • Deep learning (DL) achieved 93.0% accuracy in detecting V1 and V2 electrode misplacement in the second intercostal space.
  • Experienced physicians achieved a mean accuracy of 60% in recognizing chest electrode misplacement, significantly lower than DL.
  • DL's performance significantly surpassed that of human experts (P<.001).

Conclusions:

  • Deep learning demonstrates superior performance in identifying precordial electrode misplacement compared to experienced physicians.
  • ML and DL algorithms can serve as valuable tools to flag incorrectly recorded ECGs.
  • Implementing these algorithms can reduce diagnostic errors stemming from flawed ECG data.