EMG-Derived Respiration Signal Using the Fixed Sample Entropy during an Inspiratory Load Protocol

Area of Science:

• Biomedical Engineering
• Physiology
• Signal Processing

Background:

• Assessing respiratory muscle function typically requires complex instrumentation.
• Developing integrated technologies for non-invasive clinical measurements is a key goal.
• Extracting vital signs like respiratory rate from single measurements simplifies clinical practice.

Purpose of the Study:

• To introduce and evaluate the fixed sample entropy (fSampEn) method for deriving respiratory activity from diaphragm electromyography (EMGdi).
• To assess fSampEn's ability to extract respiratory rate non-invasively.
• To compare the performance of fSampEn-derived respiration with ECG-derived respiration (EDR).

Main Methods:

• Applied fSampEn to diaphragm EMG (EMGdi) signals during an inspiratory load protocol.
• Derived EMG-derived respiration (EMGDR) and ECG-derived respiration (EDR) signals.
• Utilized cross-correlation to compare derived signals with mouth pressure (P mouth) for similarity and lag.

Main Results:

• EMGDR signals demonstrated higher correlation (≥ 0.91) and lower lag (≤ 0.70 s) compared to EDR signals (≥ 0.83 and ≤ 0.99 s) against P mouth.
• Respiratory rate estimations from P mouth, EDR, and EMGDR signals were highly comparable.
• fSampEn proved less sensitive to cardiac activity (ECG) interference.

Conclusions:

• The fSampEn method effectively derives the respiration waveform from respiratory muscle electrical activity.
• This approach offers a promising non-invasive method for respiratory monitoring.
• fSampEn can simplify the assessment of respiratory muscle function and vital signs.