Evaluation of Respiratory Muscle Activation Using Respiratory Motor Control Assessment (RMCA) in Individuals with Chronic Spinal Cord Injury

Abstract

Short Abstract The purpose of this publication is to present our original work on a multi-muscle surface electromyographic approach to quantitatively characterize respiratory muscle activation patterns in individuals with chronic spinal cord injury using vector-based analysis. Long Abstract During breathing, activation of respiratory muscles is coordinated by integrated input from the brain, brainstem, and spinal cord. When this coordination is disrupted by spinal cord injury (SCI), control of respiratory muscles innervated below the injury level is compromised1,2 leading to respiratory muscle dysfunction and pulmonary complications. These conditions are among the leading causes of death in patients with SCI3. Standard pulmonary function tests that assess respiratory motor function include spirometrical and maximum airway pressure outcomes: Forced Vital Capacity (FVC), Forced Expiratory Volume in one second (FEV1), Maximal Inspiratory Pressure (PImax) and Maximal Expiratory Pressure (PEmax)4,5. These values provide indirect measurements of respiratorymuscle performance6. In clinical practice and research, a surface electromyography (sEMG) recorded from respiratory muscles can be used to assess respiratory motor function and help to diagnose neuromuscular pathology. However, variability in the sEMG amplitude inhibits efforts to develop objective and direct measures of respiratory motor function6. Based on a multi-muscle sEMG approach to characterize motor control of limb muscles7, known as the voluntary response index (VRI)8, we developed an analytical tool to characterize respiratory motor control directly from sEMG data recorded from multiple respiratory muscles during the voluntary respiratory tasks. We have termed this the Respiratory Motor Control Assessment (RMCA)9. This vector analysis method quantifies the amount and distribution of activity across muscles and presents it in the form of an index that relates the degree to which sEMG output within a test-subject resembles that from a group of healthy (non-injured) controls. The resulting index value has been shown to have high face validity, sensitivity and specificity9–11. We showed previously9 that the RMCA outcomes significantly correlate with levels of SCI and pulmonary function measures. We are presenting here the method to quantitatively compare post-spinal cord injury respiratory multi-muscle activation patterns to those of healthy individuals.

Respiratory motor training and neuromuscular plasticity in patients with chronic obstructive pulmonary disease: A pilot study

“…sEMG input was amplified with a gain of 2000, filtered at 4–1000 Hz and sampled at 2000 Hz. The overall amount of sEMG Magnitude (μV, Mag) and the Similarity Index (SI), that quantitate the multi-muscle distribution of activation during Maximum Expiratory Pressure Task (MEPT) in COPD subject compared to that of healthy subjects were calculated using vector-based analysis as previously described (Aslan et al, 2013; Ovechkin et al, 2010). In brief: multi-muscle activity parameters were calculated using averaged sEMG amplitudes using root mean squared (RMS) algorithm from each SCI subject for comparison to group values from NI subjects.…”

Baroreceptor reflex during forced expiratory maneuvers in individuals with chronic spinal cord injury

“…However, there are no reports in the literature to indicate whether SCI would render the Valsalva maneuver difficult (Grimm et al, 1998; Johnson et al, 1969; Previnaire et al, 2012). Our previous work has shown that the majority of individuals with chronic SCI are able to generate substantial and sustained airway pressure during the MEP maneuver (Aslan et al, 2013; Ovechkin et al, 2010). …”

Respiratory functional and motor control deficits in children with spinal cord injury

“…Latex-free, hypoallergenic woven tape (BSN medical) was used to secure the electrodes in place. The sEMG signals were sampled at 2000Hz and mean rectified for burst duration and root mean square values were calculated (Aslan et al, 2013). …”