Neural Offset Time Evaluation in Surface Respiratory Signals during Controlled Respiration

Area of Science:

• Respiratory Physiology
• Biomedical Engineering
• Signal Processing

Background:

• Surface electromyography of the diaphragm (sEMGdi) offers insights into neural respiratory drive.
• Estimating neural inspiratory time offset (ntoff) from sEMGdi is crucial for analyzing ventilatory patterns.
• Fixed sample entropy (fSampEn) is used to quantify sEMGdi amplitude variations, minimizing cardiac interference.

Purpose of the Study:

• To analyze and optimize the detection methods for neural inspiratory time offset (ntoff) from sEMGdi.
• To investigate the impact of different thresholds and fSampEn parameters on ntoff detection accuracy.
• To compare the novel ntoff detection method with airflow-based offset time (toff).

Main Methods:

• sEMGdi data was analyzed using fixed sample entropy (fSampEn) with varying parameters (r from 0.05 to 0.6, m=1, window=250 ms).
• ntoff detection was assessed using thresholds ranging from 40% to 100% of the fSampEn peak.
• A controlled respiratory protocol was employed, varying fractional inspiratory time while maintaining a constant respiratory rate (16 bpm).

Main Results:

• Optimal threshold values for ntoff detection were found to be between 66.0% and 77.0% of the fSampEn peak.
• fSampEn parameters with r values between 0.25 and 0.50 were identified as suitable for this analysis.
• The study established a reliable range for detecting ntoff, correlating well with airflow-derived offset times.

Conclusions:

• The proposed method provides a more robust and accurate approach to determining neural inspiratory time offset from sEMGdi.
• The identified optimal thresholds and fSampEn parameters enhance the reliability of respiratory drive assessment.
• This research contributes to a better understanding of respiratory control mechanisms through improved sEMGdi signal analysis.