ReSurfEMG

ReSurfEMG provides an open-source Python library for denoising, feature extraction, and quality assessment of respiratory surface electromyography (sEMG) data, facilitating reproducible signal processing and standardization in respiratory sEMG analysis.

Key Features:

• Denoising: Implements signal filtering with default settings optimized for respiratory sEMG, with systematic assessment of over-filtering and under-filtering effects on waveform characteristics.
• Feature Extraction: Extracts sEMG features including 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.
• Quality Assessment: Evaluates signal quality through metrics such as pseudo-SNR, AUB, and bell-curve error to optimize processing settings.

Scientific Applications:

• Respiratory Muscle Monitoring: Supports non-invasive monitoring of respiratory muscles in patients with respiratory failure through surface electromyography analysis.
• Ventilatory Support Optimization: Enables assessment of the interplay between mechanical ventilators and respiratory muscles to tailor ventilatory support.
• Signal Processing Standardization: Facilitates reproducible and standardized respiratory sEMG analysis across research and clinical settings.

Methodology:

The library integrates denoising, feature extraction, and quality assessment methods dedicated to respiratory sEMG analysis, with default settings validated through systematic comparison against over-filtering and under-filtering conditions.