Influence of Parameter Selection in Fixed Sample Entropy of Surface Diaphragm Electromyography for Estimating Respiratory Activity

Abstract

Fixed sample entropy (fSampEn) is a robust technique that allows the evaluation of inspiratory effort in diaphragm electromyography (EMGdi) signals, and has potential utility in sleep studies. To appropriately estimate respiratory effort, fSampEn requires the adjustment of several parameters. The aims of the present study were to evaluate the influence of the embedding dimension m, the tolerance value r, the size of the moving window, and the sampling frequency, and to establish recommendations for estimating the respiratory activity when using the fSampEn on surface EMGdi recorded for different inspiratory efforts. Values of m equal to 1 and r ranging from 0.1 to 0.64, and m equal to 2 and r ranging from 0.13 to 0.45, were found to be suitable for evaluating respiratory activity. fSampEn was less affected by window size than classical amplitude parameters. Finally, variations in sampling frequency could influence fSampEn results. In conclusion, the findings suggest the potential utility of fSampEn for estimating muscle respiratory effort in further sleep studies.

Performance Evaluation of Fixed Sample Entropy in Myographic Signals for Inspiratory Muscle Activity Estimation

“…Sampling frequency is another parameter to be taken into account in fSampEn calculation. It has been reported that higher sampling frequencies yield lower fSampEn values [26]. However, the effect of sampling frequency in fSampEn time-series can be omitted if sampling frequency is correctly chosen based on the effective bandwidth of each type of myographic respiratory signal, as in the present study.…”

“…The m parameter is typically set at 1 or 2 [16,37]. In recent studies by our group, no relevant differences were found between m = 1 and m = 2 for fSampEn calculation in sEMG lic signals [26], and m = 2 has been properly used for the analysis of different myographic respiratory signals [6]. Accordingly, m = 2 has also been used in the present study for the analysis of myographic respiratory signals.…”

“…However, even for the same application, such as respiratory muscle activity estimation, several values have been suggested for fSampEn parameters. A previous study by our group demonstrated that r and window length are the most critical parameters, influencing the magnitude and shape of fSampEn time-series [26]. It was reported that a window length of 1 s and either m equal to 1 and r ranging from 0.1 to 0.64 or m equal to 2 and r ranging from 0.13 to 0.45 could be suitable values for respiratory muscle activity estimation using fSampEn in sEMG lic signals.…”

“…Different combinations of window lengths between 0.1 s and 0.5 s were tested, and a window length of 0.5 s maximised the performance of fSampEn in all study participants and myographic respiratory signals. In general, the longer the window, the smoother the fSampEn time-series [26]. Long windows are therefore desirable to minimise the influence of cardiac noise or short-time variations of myographic respiratory signals, e.g., in neural respiratory drive estimation [9] or in inspiratory muscle activity estimation from the area under the curve of inspiratory EMG and MMG signals [6].…”

“…fSampEn has also been proposed to estimate inspiratory muscle mechanical activation efficiency from inspiratory muscle MMG signals acquired in COPD patients [19]. Furthermore, fSampEn has been used for the analysis of non-respiratory muscle activity [20][21][22][23][24].Despite the potential for using fSampEn to analyse respiratory muscle EMG and MMG signals, there is no consensus standard for optimum fSampEn parameters, and several values have been suggested for window length, m and r. Previous studies [6,9,15,[17][18][19]25] on respiratory muscles set m either at 1 or 2, r ranging from 0.1 to 0.5 and window length ranging from 0.25 to 1 s. A recent study evaluated the influence of window length, m, r and the sampling frequency on the estimation of respiratory activity from sEMG lic signals using fSampEn [26], demonstrating that window length and r are the most critical parameters determining the shape and magnitude of fSampEn time-series.…”