Improvement in Neural Respiratory Drive Estimation From Diaphragm Electromyographic Signals Using Fixed Sample Entropy

Abstract

Diaphragm electromyography is a valuable technique for the recording of electrical activity of the diaphragm. The analysis of diaphragm electromyographic (EMGdi) signal amplitude is an alternative approach for the quantification of the neural respiratory drive (NRD). The EMGdi signal is, however, corrupted by electrocardiographic (ECG) activity, and this presence of cardiac activity can make the EMGdi interpretation more difficult. Traditionally, the EMGdi amplitude has been estimated using the average rectified value (ARV) and the root mean square (RMS). In this study, surface EMGdi signals were analyzed using the fixed sample entropy (fSampEn) algorithm, and compared to the traditional ARV and RMS methods. The fSampEn is calculated using a tolerance value fixed and independent of the standard deviation of the analysis window. Thus, this method quantifies the amplitude of the complex components of stochastic signals (such as EMGdi), and being less affected by changes in amplitude due to less complex components (such as ECG). The proposed method was tested in synthetic and recorded EMGdi signals. fSampEn was less sensitive to the effect of cardiac activity on EMGdi signals with different levels of NRD than ARV and RMS amplitude parameters. The mean and standard deviation of the Pearson's correlation values between inspiratory mouth pressure (an indirect measure of the respiratory muscle activity) and fSampEn, ARV, and RMS parameters, estimated in the recorded EMGdi signal at tidal volume (without inspiratory load), were 0.38±0.12, 0.27±0.11 , and 0.11±0.13, respectively. Whereas at 33 cmH2O (maximum inspiratory load) were 0.83±0.02, 0.76±0.07, and 0.61±0.19 , respectively. Our findings suggest that the proposed method may improve the evaluation of NRD.

EMG-derived respiration signal using the fixed sample entropy during an Inspiratory load protocol

“…To alleviate the aforementioned problems, we have recently proposed the use of the fixed sample entropy (fSampEn) [11]. fSampEn has the advantage of being less sensitive in the presence of the ECG in comparison to ARV and RMS amplitude based parameters, and therefore with the potential of being used for the evaluation of EMGdi signal and the quantification of the neural respiratory drive [11]. Fig.…”

“…The fSampEn is a variation of the sample entropy (SampEn) [9]. SampEn makes use of two parameters, the embedding dimension m, and the tolerance value r. fSampEn is evaluated in a moving window, using fixed r values independent of the standard deviation of each moving window [11]. Furthermore, fSampEn has successfully been used for the evaluation of the diaphragm mechanomyographic activity, the mechanical counterpart of the respiratory muscles [10].…”

“…EMGdi signals were evaluated using a moving window of one second in length, with an overlap of 90%. The m and r values were set to 1 and 0.3 times the standard deviation of the EMGdi signal, respectively, based on previous studies [10], [11]. In addition to providing information related to the neural respiratory drive, the use of fSampEn can be used to derive the breathing activity and therefore to extract the respiratory rate.…”

“…To overcome this shortcoming, in this work, we propose the use of the fixed sample entropy (fSampEn) [10], [11], a technique based on the sample entropy [9]. The use of the fSampEn has proven to be less sensitive in the presence of cardiac activity in comparison to the average rectified value (ARV) and the root mean square (RMS) parameters [10], [11].…”

Investigating Aging-Related Changes in the Coordination of Agonist and Antagonist Muscles Using Fuzzy Entropy and Mutual Information

“…According to our previous work [16,17], N = 1000, n = 2 and m = 2 were set in this study, and r = 0.15 was set as a fixed tolerance value that was dependent on the global standard deviation of the entire segment data, and this r value would not change among different analysis windows [11]. As the sampling rate of EMG data was 1000 Hz, mean FuzzyEn values of 10 s EMG data in a trial was calculated by averaging the FuzzyEn values of EMG data of each second.…”