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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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Electrocardiogram Fundamentals01:28

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Introduction
An electrocardiogram (ECG) is a diagnostic tool for identifying cardiac conditions such as arrhythmias, conduction abnormalities, and myocardial ischemia.
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Cardiac action potentials are essential for proper heart function, enabling the rhythmic contractions needed for adequate blood circulation. Nodal cells and Purkinje fibers, specialized for electrical conduction, generate these action potentials.
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An electrocardiogram (ECG)graphically represents the heart's electrical activity on ECG paper or a monitor.
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Related Experiment Video

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Acquisition and Semi-Automated Analysis of Respiratory Muscle Surface Electromyography
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A combination method for electrocardiogram rejection from surface electromyogram.

Sara Abbaspour1, Ali Fallah1

  • 1Department of Biomedical Engineering, Amirkabir University of Technology, Tehran, Iran.

The Open Biomedical Engineering Journal
|April 29, 2014
PubMed
Summary
This summary is machine-generated.

A novel method combining artificial neural networks and wavelet transform effectively removes electrocardiogram (ECG) artifacts from electromyogram (EMG) signals. This technique enhances EMG signal quality for muscles near the heart, improving diagnostic accuracy.

Keywords:
Contaminationelectrocardiogram artifactelectromyogram signalneural networknoise removalwavelet technique.

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Area of Science:

  • Biomedical Engineering
  • Signal Processing
  • Neuroscience

Background:

  • Electrocardiogram (ECG) signals interfere with electromyogram (EMG) recordings from muscles near the heart.
  • This interference, or artifact, renders EMG signals impure and unusable for analysis.
  • Accurate EMG signal acquisition is crucial for understanding muscle electrical activity.

Purpose of the Study:

  • To develop a novel method for eliminating ECG artifacts from EMG signals.
  • To improve the quality and reliability of EMG recordings from cardiac proximity.
  • To validate the proposed method against existing artifact removal techniques.

Main Methods:

  • A hybrid approach combining artificial neural networks (ANN) and wavelet transform was developed.
  • ANN was employed for initial noise reduction, removing substantial interference.
  • Wavelet transform was utilized to eliminate residual low-frequency noise components.

Main Results:

  • The proposed method achieved a signal-to-noise ratio (SNR) of 15.6015.
  • A relative error of 0.0139 was recorded, indicating high accuracy.
  • Qualitative and quantitative criteria (power spectrum density, coherence) were used for comparison.

Conclusions:

  • The combined ANN and wavelet transform method effectively removes ECG artifacts from EMG signals.
  • This approach significantly enhances EMG signal quality for muscles near the heart.
  • The method offers a promising solution for obtaining cleaner EMG data in challenging recording conditions.