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A new Python package, ReSurfEMG, standardizes respiratory surface electromyography (sEMG) analysis for mechanical ventilation. It offers reproducible signal processing, improving monitoring of respiratory muscles during ventilation.

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Area of Science:

  • Biomedical Engineering
  • Respiratory Physiology

Background:

  • Mechanical ventilation in respiratory failure requires balancing muscle loading and gas exchange.
  • Surface electromyography (sEMG) non-invasively monitors respiratory muscles but lacks standardized processing.
  • Interplay between ventilator and respiratory muscles is crucial for effective ventilatory support.

Purpose of the Study:

  • Introduce the open-source Python package, ReSurfEMG, for respiratory sEMG analysis.
  • Address challenges in sEMG signal processing standardization and documentation.
  • Evaluate the impact of filtering settings on sEMG features.

Main Methods:

  • Developed ReSurfEMG, integrating denoising, feature extraction, and quality assessment.
  • Compared over-filtering, under-filtering, and ReSurfEMG default settings.
  • Assessed effects on waveform duration, time-to-peak, amplitude, ETP, pseudo-slope, pseudo-SNR, AUB, and bell-curve error.

Main Results:

  • Under-filtering increased amplitude (+21%) and ETP (+10%).
  • Over-filtering smoothed waveforms, reducing amplitude (-58%), ETP (-39%), and pseudo-slope (-49%).
  • ReSurfEMG defaults yielded highest SNRs with comparable or lower AUB and bell-curve errors.

Conclusions:

  • ReSurfEMG integrates advanced methods for dedicated respiratory sEMG analysis.
  • Signal processing settings significantly impact sEMG features.
  • ReSurfEMG promotes reproducible signal processing and standardization in respiratory sEMG analysis.