# ReSurfEMG: A Python Package for Comprehensive Analysis of Respiratory Surface EMG

## TL;DR

ReSurfEMG is a Python package that improves the analysis of respiratory surface EMG signals by providing standardized and reproducible methods.

## Contribution

ReSurfEMG introduces a comprehensive, open-source tool for respiratory sEMG analysis with standardized signal processing.

## Key findings

- Under-filtering increases sEMG amplitudes and electrical time product by 21% and 10%, respectively.
- Over-filtering reduces amplitude, ETP, and pseudo-slope by 58%, 39%, and 49%, respectively.
- Default ReSurfEMG settings provide the highest signal-to-noise ratios with minimal baseline errors.

## Abstract

What are the main findings?

ReSurfEMG is a comprehensive Python package for advanced respiratory sEMG analysis.

Signal processing methodology and settings profoundly affect sEMG signal quality.

What is the implication of the main finding?

ReSurfEMG facilitates comprehensive reporting as a citable methodological reference.

ReSurfEMG lays the open-source groundwork for methodological standardization.

In patients with respiratory failure, mechanical ventilation aims to balance respiratory muscle loading and gas exchange. The interplay between the ventilator and the respiratory muscles is an increasingly recognized factor in tailoring ventilatory support. Surface electromyography (sEMG) offers a non-invasive modality to monitor the respiratory muscles. The sEMG signal, however, requires elaborate processing, which is limitedly standardized and documented. This paper presents the Respiratory Surface Electromyography (ReSurfEMG) package, an open-source Python package for respiratory sEMG analysis developed to address these challenges. ReSurfEMG integrates denoising, feature extraction, and quality assessment in one dedicated library. The effects of over- and under-filtering were compared to ReSurfEMG default settings regarding waveform duration, time-to-peak, amplitude, electrical time product (ETP), pseudo-slope, pseudo-signal-to-noise ratio (SNR), area under the baseline (AUB), and bell-curve error. Under-filtering increased amplitudes (+21%) and ETPs (+10%). Over-filtering smoothed sEMG waveforms, reducing amplitude (−58%), ETP (−39%), and pseudo-slope (−49%), while waveform duration and time-to-peak increased. Default ReSurfEMG settings provided the highest SNRs with similar or lower AUBs and bell-curve errors. The ReSurfEMG library integrates advanced methods dedicated to respiratory sEMG analysis. Systematic assessment using ReSurfEMG showed that signal processing settings affect sEMG features. ReSurfEMG enables reproducible signal processing, facilitating the standardization of respiratory sEMG analysis.