Paper Info
Title
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 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 work, surface EMGdi signals were analyzed using the fixed sample entropy (fSampEn) algorithm, and compared to 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.
Year
DOI
Venue
2016
10.1109/JBHI.2015.2398934
IEEE journal of biomedical and health informatics
Keywords
Field
DocType
electromyography,diaphragm muscle,neural respiratory drive
Biomedical engineering,Approximate entropy,Sample entropy,Computer science,Artificial intelligence,Amplitude,Diaphragm (structural system),Pattern recognition,Electromyography,Speech recognition,Root mean square,Average rectified value,Standard deviation
Journal
Volume
Issue
ISSN
PP
99
2168-2208
Citations 
PageRank 
References 
7
1.03
2
Authors
4
Name
Order
Citations
PageRank
Luis Estrada1226.84
Abel Torres2196.63
Leonardo Sarlabous3186.57
R Jané413143.71