Surface mechanomyography and electromyography provide non-invasive indices of inspiratory muscle force and activation in healthy subjects

Abstract

The current gold standard assessment of human inspiratory muscle function involves using invasive measures of transdiaphragmatic pressure (Pdi) or crural diaphragm electromyography (oesEMGdi). Mechanomyography is a non-invasive measure of muscle vibration associated with muscle contraction. Surface electromyogram and mechanomyogram, recorded transcutaneously using sensors placed over the lower intercostal spaces (sEMGlic and sMMGlic respectively), have been proposed to provide non-invasive indices of inspiratory muscle activation, but have not been directly compared to gold standard Pdi and oesEMGdi measures during voluntary respiratory manoeuvres. To validate the non-invasive techniques, the relationships between Pdi and sMMGlic, and between oesEMGdi and sEMGlic were measured simultaneously in 12 healthy subjects during an incremental inspiratory threshold loading protocol. Myographic signals were analysed using fixed sample entropy (fSampEn), which is less influenced by cardiac artefacts than conventional root mean square. Strong correlations were observed between: mean Pdi and mean fSampEn |sMMGlic| (left, 0.76; right, 0.81), the time-integrals of the Pdi and fSampEn |sMMGlic| (left, 0.78; right, 0.83), and mean fSampEn oesEMGdi and mean fSampEn sEMGlic (left, 0.84; right, 0.83). These findings suggest that sMMGlic and sEMGlic could provide useful non-invasive alternatives to Pdi and oesEMGdi for the assessment of inspiratory muscle function in health and disease.

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

“…The performance of fSampEn was assessed by means of the cross-covariance of fSampEn time-series and both mouth and transdiaphragmatic pressures generated by inspiratory muscles. A set of optimal general fSampEn parameters was proposed, allowing fSampEn of different subjects to be compared and contributing to improving the assessment of inspiratory muscle activity in health and disease.Entropy 2019, 21, 183 2 of 16 mechanomyography (MMG) [5,6] and electromyography (EMG) [6-10], respectively. The surface mechanomyogram is a non-invasive measure of muscle fibre vibration during muscle contraction and is considered to be the mechanical counterpart of motor unit electrical activity as measured by surface electromyography.…”

“…The surface mechanomyogram is a non-invasive measure of muscle fibre vibration during muscle contraction and is considered to be the mechanical counterpart of motor unit electrical activity as measured by surface electromyography. Surface mechanomyogram and electromyogram of inspiratory muscles are typically measured using accelerometers and bipolar electrode pairs, respectively, positioned on the chest wall over the lower intercostal spaces (sMMG lic and sEMG lic respectively) [6] or the parasternal intercostal spaces (sMMG para and sEMG para respectively) [8].Inspiratory muscle EMG and MMG signals are, however, contaminated by cardiac noise corresponding to the electrical (ECG) and mechanical (MCG) activity of the heart, respectively. Conventional approaches to analyse EMG and MMG signals, based on amplitude estimators, such as the average rectified value (ARV) or the root mean square (RMS) [11,12], are greatly influenced by cardiac activity.…”

“…Furthermore, since the frequency content of cardiac noise overlaps greatly with that of inspiratory muscle EMG and MMG signals, it is difficult to reduce cardiac noise using conventional frequency filters. Although some automatic algorithms have been previously proposed to remove cardiac noise from EMG signals [13,14], these algorithms involve more complex filters or the recording of an extra ECG channel for QRS complexes detection.As an alternative, our group has recently proposed fixed sample entropy (fSampEn) as a method to estimate respiratory muscle function from sEMG lic [6,9] and sMMG lic [6,15] signals, attenuating interference from cardiac activity. Based on sample entropy (SampEn) [16], fSampEn is a measure of regularity and complexity of time-series signals, so that more regular signals are less complex and lead to lower values of fSampEn.…”

“…In myographic respiratory signals, fSampEn has proven to be less sensitive in quantifying amplitude variations of more deterministic signal components, such as ECG and MCG, than in quantifying amplitude variations of more complex signal components, such as inspiratory muscle EMG and MMG [9,15]. Due to this advantageous property of fSampEn, this technique has been used in several applications related to respiratory muscles in healthy subjects, such as estimation of neural respiratory drive [9] and respiratory muscle activity [6,15,17] from inspiratory muscle EMG and MMG signals acquired during incremental loaded breathing, or estimation of neural inspiratory time onset and offset from inspiratory muscle EMG signals [18]. fSampEn has also been proposed to estimate inspiratory muscle mechanical activation efficiency from inspiratory muscle MMG signals acquired in COPD patients [19].…”

“…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.…”