LAUSR

In clinic, the acquisition of airflow with nasal prongs, masks, thermistor to monitor respiratory function is more uncomfortable and inconvenience than surface diaphragm electromyography (EMGdi) using electrode pads. The EMGdi with strong electrocardiograph (ECG) interference affect the extraction of its characteristic information. In this work, surface EMGdi and airflow signals of 20 subjects were collected under 5 incremental inspiratory threshold loading protocols from quiet breathing to maximum forced breathing. First, we filtered out the ECG interference in EMGdi based on the combination of stationary wavelet transform and the positioning of ECG to obtain pure EMGdi (EMGdip). Second, the Spearman’s rank correlation coefficients between EMGdi and EMGdip quantified by time series fixed sample entropy (fSampEn), root mean square (RMS), and envelope were compared to verify the robustness of the fSampEn to ECG. A comparative analysis of correlation between fSampEn of EMGdi and inspiratory airflow and the correlation between envelope of EMGdip (EMGdie) and inspiratory airflow found that there was no significant difference between the two, indicating the feasibility of using fSampEn to predict airflow. Moreover, fSampEn of EMGdi was used as characteristic parameter to build a quantitative relationship with the airflow by polynomial regression analysis. Mean coefficient of determination of all subjects in any breathing state is greater than 0.88. Finally, nonlinear programming method was used to solve a universal fitting coefficient between fSampEn of EMGdi and airflow for each subject to further evaluate the possibility of using surface EMGdi to monitor and control respiratory activity.