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Acquisition and Semi-Automated Analysis of Respiratory Muscle Surface Electromyography
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Singular Value Decomposition for Removal of Cardiac Interference from Trunk Electromyogram.

Elisabetta Peri1, Lin Xu2, Christian Ciccarelli1

  • 1Department of Electrical Engineering, Eindhoven University of Technology, 5600 MB Eindhoven, The Netherlands.

Sensors (Basel, Switzerland)
|January 20, 2021
PubMed
Summary

A novel singular value decomposition (SVD) algorithm effectively removes cardiac noise from electromyography (EMG) signals. This SVD method surpasses existing techniques for trunk and diaphragm EMG reconstruction, aiding sleep disorder diagnosis.

Keywords:
electrocardiograph interferencequantitative assessment of performancerespiratory monitoringsingular value decompositiontrunk electromyography

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Area of Science:

  • Biomedical Engineering
  • Signal Processing
  • Physiology

Background:

  • Cardiac signals contaminate electromyography (EMG) recordings, particularly trunk EMG.
  • Accurate EMG analysis is crucial for diagnosing various physiological and neurological conditions.
  • Existing methods for cardiac artifact removal have limitations in performance and applicability.

Purpose of the Study:

  • To introduce a new Singular Value Decomposition (SVD) based algorithm for removing cardiac contamination from trunk EMG.
  • To compare the performance of the SVD algorithm against current state-of-the-art methods.
  • To evaluate the algorithm's applicability on real-world data, including diaphragm EMG for sleep apnea.

Main Methods:

  • A novel SVD-based algorithm was developed for cardiac artifact removal from EMG.
  • Performance was validated using a synthetic dataset combining ECG and EMG, and compared against gating, high-pass filtering, template subtraction (TS), and independent component analysis (ICA).
  • An experimental calibration curve was proposed to optimize SVD components based on signal-to-noise ratio (SNR).

Main Results:

  • The SVD algorithm demonstrated superior performance in reconstructing trunk EMG compared to existing methods.
  • Quantitative analysis showed improved accuracy in both time (relative mean squared error < 15%) and frequency (shift in mean frequency < 1 Hz) domains.
  • Application to diaphragm EMG in a sleep apnea patient showed significant agreement with the respiratory cycle (correlation coefficient = 0.81, p < 0.01), outperforming TS and ICA.

Conclusions:

  • The proposed SVD algorithm is highly effective for removing cardiac contamination from trunk EMG.
  • Its ability to function without a reference ECG enhances its clinical utility.
  • The algorithm shows promise for non-invasive estimation of respiratory effort in sleep-related breathing disorders.