Improvement in neural respiratory drive estimation from diaphragm electromyographic signals using fixed sample entropy
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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 inspiratorymouth 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 cmH(2)O (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.
CitationEstrada, L., Torres, A., Sarlabous, L., Jane, R. Improvement in neural respiratory drive estimation from diaphragm electromyographic signals using fixed sample entropy. "IEEE Journal of Biomedical and Health Informatics", 1 Març 2016, vol. 20, núm. 2, p. 476-485.