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Related Concept Videos

Electrocardiogram01:29

Electrocardiogram

3.3K
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...
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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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ECG Interpretation of Rhythms01:24

ECG Interpretation of Rhythms

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An electrocardiogram (ECG)graphically represents the heart's electrical activity on ECG paper or a monitor.
Components of the Electrocardiogram
The primary components of a normal ECG waveform in Normal sinus rhythm(NSR) include the P wave, PR interval, QRS complex, ST segment, T wave, and occasionally a U wave.
ECG waveforms are divided by vertical and horizontal lines at standard intervals.
The horizontal axis measures time and rate, and the vertical axis measures amplitude or voltage....
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Instrumentation Amplifier01:25

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An electrocardiography (ECG) machine is an essential piece of medical equipment used to monitor the electrical activity of the heart. It operates by detecting small electrical changes on the skin that result from the depolarization of the heart muscle during each heartbeat. However, these signals are in the microvolt range and can be easily overwhelmed by noise or interference.
To overcome this challenge, an ECG machine utilizes an instrumentation amplifier. This specialized amplifier is...
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Related Experiment Video

Updated: Sep 22, 2025

Author Spotlight: Advancing the Study of Brain-Heart Interplay with a Comprehensive EEGLAB Plugin for Multimodal Signal Analysis
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ECGAssess: A Python-Based Toolbox to Assess ECG Lead Signal Quality.

Linus Kramer1, Carlo Menon1, Mohamed Elgendi1

  • 1ETH Zürich, Biomedical and Mobile Health Technology Lab, Zurich, Switzerland.

Frontiers in Digital Health
|May 23, 2022
PubMed
Summary

This study introduces ECGAssess, a Python toolbox that helps untrained professionals record high-quality electrocardiography (ECG) signals. The tool provides feedback on signal acceptability, improving cardiovascular diagnostics when experts are unavailable.

Keywords:
anaesthesiabiomedical engineeringdata sciencedigital healthemergency and critical careintensive care unit

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Area of Science:

  • Biomedical Engineering
  • Cardiology
  • Medical Informatics

Background:

  • Electrocardiography (ECG) is crucial for diagnosing cardiovascular diseases but requires expert operation.
  • Lack of trained personnel limits ECG use, impacting healthcare quality.
  • Untrained professionals need tools to capture diagnostically useful ECGs for expert review.

Purpose of the Study:

  • To develop and evaluate ECGAssess, a Python toolbox for assessing ECG signal quality.
  • To enable untrained healthcare professionals to record diagnostically useful ECGs.
  • To provide automated feedback on ECG lead acceptability.

Main Methods:

  • Developed a Python-based toolbox, ECGAssess, to classify ECG leads as acceptable or unacceptable.
  • Classified signals based on stationarity, heart rate, and signal-to-noise ratio.
  • Trained and evaluated the toolbox using datasets of 1,200 and 4,200 ECG leads, respectively, with expert annotations.

Main Results:

  • The ECGAssess toolbox achieved over 94% accuracy in classifying 4,200 ECG leads.
  • The tool provides feedback on signal quality based on predefined criteria.
  • Identified and corrected errors in ECG device usage through user feedback.

Conclusions:

  • ECGAssess effectively assists untrained users in obtaining high-quality ECG recordings.
  • The toolbox enhances the accessibility and reliability of ECG diagnostics.
  • This tool can improve cardiovascular disease diagnosis in resource-limited settings.