Activation of respiratory muscles during weaning from mechanical ventilation

Walterspacher, Stephan; Gückler, Julia; Pietsch, Fabian; Walker, David; Kabitz, Hans‐Joachim; Dreher, Michael

doi:10.1016/j.jcrc.2016.11.033

Cited by 11 publications

(16 citation statements)

References 46 publications

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“…Our results suggest that the lack of differences in EE between lying and sitting, and higher values in RQ in the former could be due to an extra activation of ventilatory muscles (e.g. diaphragm) and to a loss of ventilatory efficiency in lying vs. sitting [30], which would also be reflected in higher RR in lying position. Postural maintenance needs a higher muscle activation sitting than lying.…”

Section: Discussionmentioning

confidence: 99%

Energy expenditure differences across lying, sitting, and standing positions in young healthy adults

et al. 2019

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The time spent in sedentary behaviour represents an important public health burden. To reduce sedentary time in the general population, the simplest, most effective, and most accessible method is to decrease lying and sitting time. We aimed to compare differences on energy expenditure (EE) across lying, sitting, and standing positions; and to analyse the associations between the change on EE of changing from one position to another and anthropometric and body composition parameters in young healthy adults. A total of 55 (69% women) young healthy adults aged 21.7 ± 2.2 participated in the study. We measured EE by indirect calorimetry across lying, sitting, and standing positions following the standard procedures. The EE was significantly higher in standing than in both lying and sitting positions (mean difference: 0.121±0.292 and 0.125±0.241 kcal/min, respectively; all P<0.001), and no differences were observed between lying and sitting positions (P = 1.000). There was a negative association between the EE differences in sitting vs. standing position and lean body mass (P = 0.048), yet no associations between EE differences with the rest of the anthropometric and body composition parameters were observed in each position pair studied (all P>0.321). Our findings support the fact that increasing the time spent standing could be a simple strategy to slightly increase EE. Therefore, our results have important clinical implications including a better monitoring, characterizing, and promoting countermeasures to sedentariness through low-level physical activities.

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Section: Discussionmentioning

confidence: 99%

Energy expenditure differences across lying, sitting, and standing positions in young healthy adults

et al. 2019

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“…Sampling rate was mentioned in twelve studies (75%) obtaining variations between 500 and 10,000 Hz [ 6 , 17 – 19 , 21 , 23 – 27 , 29 , 30 ], Bandpass filters were reported in 10 studies (62.5%) with variations between 0–1,000 Hz [ 6 , 17 , 19 , 21 , 22 , 24 – 27 , 31 ]. Only one study (6.25%) reported high-pass and low-pass filters for EMG signal processing details [ 26 ].…”

Section: Resultsmentioning

confidence: 99%

“…Regarding cardiac-interference filtering techniques to prevent EMG noise contamination, nine studies (50%) have reported a number of techniques, including gating [ 6 , 17 , 18 , 20 , 31 ], or some sort of adaptive filter [ 21 , 25 , 30 ] or even visual inspection [ 26 ]. Amplifier gain is one of the settings for high quality EMG signals with low noise.…”

Section: Resultsmentioning

confidence: 99%

“…This setting is defined as the ratio between the voltage that enters and exits the amplifier, should suit the characteristics of the experiment, the type of electrodes used, as well as the muscle studied, and not the adjusted to exceed the voltage expected from the system [ 32 ]. Gain was mentioned in three studies (18.75%) with variations between 20–1,000 times [ 6 , 26 , 27 ]. EMG data acquisition recording reported by twelve studies (75%) varied from 60 through 1.200 seconds [ 17 , 19 – 22 , 24 – 27 , 29 – 31 ] Table 2 .…”

Section: Resultsmentioning

confidence: 99%

“…Regarding the sEMG signal units, five papers (31.25%) [ 6 , 17 , 18 , 20 , 22 ] presented them in microvolts (μV), four studies (25%) [ 21 , 26 , 27 , 31 ] in percentages, and eight studies (50%) did not inform any unit [ 19 , 23 – 25 , 28 – 31 ]. The obtained variables were presented as simple mean RMS/EMG signal usually rectified or normalized [ 6 , 17 – 20 , 22 – 24 , 26 , 27 , 29 ] or integrated RMS/EMG signals—the difference between the maximum amplitude of RMS signal and its baseline (EMG MAX ), area under the EMG signal curve (EMG AUC ), peak of muscle activity during inspiration (EMG PEAK ), and end of muscle activity per breath (EMG AUC/min ) [ 19 , 21 , 28 , 30 , 31 ] Table 2 .…”

Section: Resultsmentioning

confidence: 99%

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Surface electromyography signal processing and evaluation on respiratory muscles of critically ill patients: A systematic review

Silva¹,

Campos²,

Leite³

et al. 2023

PLoS ONE

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Background Surface Electromyography (sEMG) has been used to monitor respiratory muscle function and contractility in several clinical situations, however there is the lack of standardization for the analysis and processing of the signals. Objective To summarize the respiratory muscles most assessed by sEMG in the critical care setting and the assessment procedure details employed on those muscles regarding electrode placement, signal acquisition, and data analysis. Methods A systematic review of observational studies was registered on PROSPERO (number CRD42022354469). The databases included PubMed; SCOPUS; CINAHL, Web of Science and ScienceDirect. Two independent reviewers ran the quality assessment of the studies using the Newcastle-Ottawa Scale and Downs & Black checklists. Results A total of 311 participants were involved across the 16 studies, from which 62.5% (10) assessed the diaphragm muscle and 50% (8) assessed the parasternal muscle with similar electrode placement in both of them. We did not identify common patterns for the location of the electrodes in the sternocleidomastoid and anterior scalene muscles. 12/16 reported sample rate, 10/16 reported band-pass and 9/16 reported one method of cardiac-interference filtering technique. 15/16 reported Root Mean Square (RMS) or derivatives as sEMG-obtained variables. The main applicabilities were the description of muscle activation in different settings (6/16), testing of reliability and correlation to other respiratory muscles assessment techniques (7/16), and assessment of therapy response (3/16). They found sEMG feasible and useful for prognosis purposes (2/16), treatment guidance (6/16), reliable monitoring under stable conditions (3/16), and as a surrogate measure (5/16) in mechanically ventilated patients in elective or emergency invasive procedures (5/16) or in acute health conditions (11/16). Conclusions The diaphragm and parasternal muscles were the main muscles studied in the critical care setting, and with similar electrodes placement. However, several different methods were observed for other muscles electrodes placement, sEMG signals acquisition and data analysis.

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