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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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Correlation between ECG and Cardiac Cycle01:25

Correlation between ECG and Cardiac Cycle

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The electrical signals recorded on an electrocardiogram (ECG) occur before the mechanical processes of contraction and relaxation during the cardiac cycle.
A cardiac action potential originates in the SA node and spreads throughout the atria and the AV node in approximately 0.03 seconds. This results in the P wave in an ECG and triggers atrial contraction. The action potential is then briefly slowed at the AV node, allowing the atria to contract and fill the ventricles with blood before...
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Instrumentation Amplifier01:25

Instrumentation Amplifier

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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

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Determining the Functional Status of the Corticospinal Tract Within One Week of Stroke
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Electromyography Parameter Variations with Electrocardiography Noise.

Kang-Ming Chang1,2,3, Peng-Ta Liu4,5, Ta-Sen Wei5

  • 1Department of Computer Science and Information Engineering, Asia University, Taichung 41354, Taiwan.

Sensors (Basel, Switzerland)
|August 26, 2022
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Summary
This summary is machine-generated.

Electrocardiographic (ECG) signals contaminate electromyograms (EMG). This study simulated ECG noise in EMG signals, finding that high signal-to-noise ratios (SNR) allow for accurate EMG feature extraction, crucial for reliable analysis.

Keywords:
electrocardiographyelectromyographynoise

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Area of Science:

  • Biomedical Engineering
  • Signal Processing
  • Neuroscience

Background:

  • Electromyogram (EMG) signals are often contaminated by electrocardiographic (ECG) signals.
  • Traditional filters struggle to separate these signals due to overlapping spectral components.
  • Accurate extraction of ECG noise from EMG is a significant challenge in signal processing.

Purpose of the Study:

  • To simulate EMG signals contaminated with varying levels of ECG noise.
  • To investigate the impact of ECG contamination on EMG signal features.
  • To evaluate the effectiveness of different ECG removal methods.

Main Methods:

  • Simulated 32 datasets by combining EMG and ECG signals from the MIT-BIH Noise Stress Test (NSTD) Database with varying EMG/ECG signal-to-noise ratios (SNR).
  • Applied four ECG removal techniques post-R-peak detection using the Pan-Tompkins algorithm.
  • Calculated 13 time-domain and 4 frequency-domain EMG features from denoised signals and compared them to clean EMG features.

Main Results:

  • At EMG/ECG SNR ratios of 10 and 20, ECG contamination was negligible, with denoised EMG features showing high similarity (close to 1) to clean EMG.
  • Lower EMG/ECG SNR ratios (1 and 2) resulted in significant variations in EMG features.
  • The study quantifies the impact of ECG noise on EMG feature reliability across different SNR levels.

Conclusions:

  • ECG removal methods are effective in preserving EMG features at higher SNR levels.
  • Understanding the relationship between EMG/ECG SNR and feature variation is critical for accurate EMG analysis.
  • These simulation results provide valuable insights for interpreting EMG data in the presence of cardiac noise.