Evidence towards Improved Estimation of Respiratory Muscle Effort from Diaphragm Mechanomyographic Signals with Cardiac Vibration Interference Using Sample Entropy with Fixed Tolerance Values

Abstract

The analysis of amplitude parameters of the diaphragm mechanomyographic (MMGdi) signal is a non-invasive technique to assess respiratory muscle effort and to detect and quantify the severity of respiratory muscle weakness. The amplitude of the MMGdi signal is usually evaluated using the average rectified value or the root mean square of the signal. However, these estimations are greatly affected by the presence of cardiac vibration or mechanocardiographic (MCG) noise. In this study, we present a method for improving the estimation of the respiratory muscle effort from MMGdi signals that is robust to the presence of MCG. This method is based on the calculation of the sample entropy using fixed tolerance values (fSampEn), that is, with tolerance values that are not normalized by the local standard deviation of the window analyzed. The behavior of the fSampEn parameter was tested in synthesized mechanomyographic signals, with different ratios between the amplitude of the MCG and clean mechanomyographic components. As an example of application of this technique, the use of fSampEn was explored also in recorded MMGdi signals, with different inspiratory loads. The results with both synthetic and recorded signals indicate that the entropy parameter is less affected by the MCG noise, especially at low signal-to-noise ratios. Therefore, we believe that the proposed fSampEn parameter could improve estimates of respiratory muscle effort from MMGdi signals with the presence of MCG interference.

Influence of subcutaneous fat on mechanomyographic signals at three levels of voluntary effort

“…Therefore, it is important to consider the effects of fat when adopting MMG as a tool in applications such as prostheses control (Orizio et al, 2003), monitoring of muscular fatigue (Orizio et al, 2003;Scheeren et al, 2010;Tarata, 2003), neuromuscular diseases diagnosis (Hu et al, 2007), respiratory muscle work (Sarlabous et al, 2014), among others. Subcutaneous fat, as demonstrated in this study, interferes on the analysis.…”

“…MMG is used in many applications, including: prostheses control (Orizio et al, 2003); indication of muscle activation degree (Scheeren et al, 2010); monitoring of muscle fatigue (Orizio et al, 2003;Tarata, 2003); neuromuscular diseases diagnosis (Hu et al, 2007); obtainment of signals for the study of muscle strength gradation mechanisms (Akataki et al, 2001;Matta et al, 2005;Madeleine et al, 2001;Nogueira-Neto et al, 2013); evaluation of respiratory muscle work (Sarlabous et al, 2014); changes in MU activation strategies, which can occur with aging, neuromuscular diseases, endurance training programs, and care of injuries (Cooper et al, 2014); and Parkinson's disease (Marusiak et al, 2009;Malek and Coburn, 2012). In applications with comparisons between the MMG signal and the force response the biceps brachii (Orizio et al, 1989) and rectus femoris (Krueger et al, 2016;Shin et al, 2016) are the muscles more used due its easy accessibility and comparison capability.…”

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

“…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%.…”

“…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].…”