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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.
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
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Acquisition and Semi-Automated Analysis of Respiratory Muscle Surface Electromyography
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Wavelet-based unsupervised learning method for electrocardiogram suppression in surface electromyograms.

Maciej Niegowski1, Miroslav Zivanovic1

  • 1Deptartment Ingeniería Eléctrica y Electrónica, Universidad Pública de Navarra Campus Arrosadía, 31006 Pamplona, Spain.

Medical Engineering & Physics
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Summary

This study introduces a new unsupervised learning method using wavelet transforms and non-negative matrix factorization to remove electrocardiogram (ECG) noise from electromyogram (EMG) signals, improving signal quality.

Keywords:
Electrocardiogram removalElectromyographyNon-negative matrix factorizationWavelets

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Area of Science:

  • Biomedical Engineering
  • Signal Processing
  • Machine Learning

Background:

  • Surface electromyogram (EMG) recordings are often contaminated by electrocardiogram (ECG) artifacts.
  • Accurate EMG analysis requires effective removal of these ECG perturbations.
  • Existing unsupervised methods face challenges in robustly separating ECG from EMG signals.

Purpose of the Study:

  • To develop a novel unsupervised learning approach for removing ECG interference from single-channel EMG recordings.
  • To enhance the quality of EMG signals for more reliable physiological analysis.
  • To provide a parameter-free and readily applicable method for ECG-EMG separation.

Main Methods:

  • Utilizing wavelet decomposition to create sparse time-frequency representations of ECG signals.
  • Applying non-negative matrix factorization (NMF) on wavelet-based intensity images for pattern extraction.
  • Implementing a novel robust initialization strategy for NMF to prevent convergence issues.
  • Evaluating the method on real EMG data against state-of-the-art unsupervised algorithms and singular spectrum analysis.

Main Results:

  • The proposed wavelet-NMF method demonstrated superior performance in ECG-EMG separation compared to reference algorithms.
  • Quantitative metrics including high-to-low energy ratio, normalized median frequency, spectral power difference, and normalized average rectified value showed improvement.
  • The method proved effective across a range of ECG contamination levels.

Conclusions:

  • The novel unsupervised learning approach effectively removes ECG perturbations from EMG signals.
  • The wavelet-NMF method offers a robust and user-friendly solution for ECG-EMG separation.
  • This technique advances the accuracy of surface EMG signal analysis in the presence of cardiac noise.