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Instrumentation Amplifier01:25

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Acquisition and Semi-Automated Analysis of Respiratory Muscle Surface Electromyography
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A new algorithm for ECG interference removal from single channel EMG recording.

Shayan Yazdani1, Mahmood Reza Azghani2, Mohammad Hossein Sedaaghi1

  • 1Department of Electrical Engineering, Sahand University of Technology, Tabriz, Iran.

Australasian Physical & Engineering Sciences in Medicine
|July 23, 2017
PubMed
Summary
This summary is machine-generated.

This study introduces a novel method using progressive image denoising (PID) and ensemble empirical mode decomposition (EEMD) to eliminate electrocardiogram (ECG) interference from electromyogram (EMG) signals, significantly improving signal quality.

Keywords:
Artifact removalDenoisingEEMDElectrocardiogramElectromyogramPID

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Area of Science:

  • Biomedical Engineering
  • Signal Processing

Background:

  • Electromyogram (EMG) signal acquisition, particularly from trunk muscles, is often contaminated by electrocardiogram (ECG) interference.
  • Existing methods for ECG interference removal from EMG signals have limitations in effectiveness and applicability.

Purpose of the Study:

  • To develop and evaluate a new algorithm for effectively removing ECG interference from EMG signals.
  • To introduce the novel application of the progressive image denoising (PID) algorithm in 1D signal processing for biomedical applications.

Main Methods:

  • The proposed method combines the progressive image denoising (PID) algorithm with ensemble empirical mode decomposition (EEMD).
  • PID, typically used for image denoising, is adapted by modeling EMG signal amplitude as white Gaussian noise.
  • The algorithm's performance is benchmarked against established methods like High-Pass Filtering (HPF), EEMD-ICA, and Wavelet-ICA.

Main Results:

  • The novel PID-EEMD algorithm demonstrated superior performance in removing ECG interference from EMG signals compared to existing methods.
  • Performance was evaluated using Normalized Mean Square Error (NMSE), Signal-to-Noise Ratio (SNR), and Pearson Correlation Coefficient (PCC).
  • The proposed method achieved better results across all three evaluation metrics.

Conclusions:

  • The integration of PID and EEMD offers a highly effective solution for ECG interference suppression in EMG recordings.
  • This approach represents a significant advancement in signal processing for biomedical applications, particularly in myography.
  • The study highlights the potential of adapting image processing techniques for complex biological signal denoising.