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Volume 14 No. 07
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Scientific Investigations

Quantitative Analysis of Thoracoabdominal Asynchrony in Pediatric Polysomnography

Jason Z. Bronstein, MD1; Li Xie, ScM2; Thomas H. Shaffer, MSE, PhD3; Aaron Chidekel, MD1; Robert Heinle, MD1
1Division of Pulmonology, Nemours/Alfred I. duPont Hospital for Children, Wilmington, Delaware; 2Biostatistics Core, Nemours Biomedical Research, Nemours/Alfred I. duPont Hospital for Children, Wilmington, Delaware; 3Center for Pediatric Lung Research, Nemours/Alfred I. duPont Hospital for Children, Wilmington, Delaware

ABSTRACT

Study Objectives:

Objective measurements of thoracoabdominal asynchrony (TAA), such as average phase angle (θavg), can quantify airway obstruction. This study demonstrates and evaluates use of θavg for predicting obstructive sleep apnea (OSA) in pediatric polysomnography (PSG).

Methods:

This prospective observational study recruited otherwise healthy 3- to 8-year-old children presenting for PSG due to snoring, behavioral problems, difficulty sleeping, and/or enlarged tonsils. Respiratory inductance plethysmography (RIP) was directly monitored and data were collected during each PSG. θavg and average labored breathing index (LBIavg) were calculated for earliest acceptable 5-minute periods of stage N3 sleep and stage R sleep. Associations between θavg and obstructive apnea index (OAI) and obstructive apnea-hypopnea index (OAHI), as well as between LBIavg and OAI and OAHI, were examined.

Results:

Forty patients undergoing PSG were analyzed. Thirty percent of patients had OSA, 57.5% had enlarged tonsils, and 17.5% were obese. θavg during stage N3 sleep and stage R sleep had significant positive correlations with OAI (Spearman r = .35 [P = .03] and .40 [P = .01], respectively) and θavg during stage N3 sleep with OAHI (r = .35 [P = .03]). LBIavg showed lower correlations. Median θavg during stage R sleep (33.1) was significantly greater than during stage N3 sleep (13.7, P = .0005).

Conclusions:

Association of θavg with OAI and OAHI shows that θavg reflects airway obstruction and has potential use as a quantitative indicator of OSA. RIP provides valuable information that is readily available in PSG. The significant difference between θavg in stage N3 sleep and stage R sleep confirms the clinical observation that there is more asynchrony during rapid eye movement sleep than non-rapid eye movement sleep.

Citation:

Bronstein JZ, Xie L, Shaffer TH, Chidekel A, Heinle R. Quantitative analysis of thoracoabdominal asynchrony in pediatric polysomnography. J Clin Sleep Med. 2018;14(7):1169–1176.


BRIEF SUMMARY

Current Knowledge/Study Rationale: Current practice is to use polysomnographic respiratory inductance plethysmography data qualitatively, though there is untapped potential to use easily calculable quantitative measures for further information on thoracoabdominal asynchrony and airway obstruction during pediatric polysomnography in common clinical groups. This study was performed to demonstrate feasibility of these methods and utility of the measures.

Study Impact: This study provides evidence that average phase angle in non-rapid eye movement and rapid eye movement sleep correlates with indices of obstructive sleep apnea. This study quantifies the difference in asynchrony between non-rapid eye movement and rapid eye movement sleep. Average phase angle is a feasible measure in pediatric polysomnography.

INTRODUCTION

Obstructive sleep apnea (OSA) is a major public health problem, affecting approximately 1% to 6% of all children and 2% to 24% of adults.14 OSA is responsible for billions of dollars of health care costs as it contributes to cardiovascular disease, metabolic syndrome, motor vehicle accidents, and perioperative morbidity and mortality.5 It is implicated in decreased school performance, potentially lifelong cognitive impairment, and possibly decreased life expectancy.68

Attended nocturnal polysomnography (PSG) (≥ 7 channels) is the gold standard for diagnosing OSA; however, it has limitations. Problems include the relative unavailability of well-equipped pediatric laboratories and trained personnel, as well as the large amount of time and labor associated with the procedure—not to mention high cost and inconvenience to patients.9 Children often remove the measurement devices during the course of the study, leading to a need for vigilant observation and intervention by the sleep technologist. Sometimes data are suboptimal despite these measures. Innovative improvements in pediatric OSA screening and diagnosis are needed.

Respiratory inductance plethysmography (RIP) is a noninvasive test of pulmonary function that does not require effort from the patient. It is currently used as the American Academy of Sleep Medicine recommended standard for measuring respiratory effort during PSG, and is arguably the easiest and most reliable method.10,11 A RIP band consists of a sinusoid wire coil insulated in elastic. Typically, one band is worn around the chest at the level of the nipple and another around the abdomen near the umbilicus.12 Dynamic stretching of the bands creates digital waveforms due to change in self-inductance and oscillatory frequency of the electronic signal. Changes in thoracic/ abdominal diameter, cross-sectional area, and circumference are all directly proportional to changes in lung volume, so with normal tidal breathing, amplitude of the waveform correlates with tidal volume.13

RIP can be used to quantify thoracoabdominal asynchrony (TAA), which is an indirect measure of pulmonary function. Data routinely collected by RIP during PSG are typically used qualitatively to denote presence or absence of respiratory effort or, sometimes, subjective appearance of asynchrony.10,14 Average phase angle (θavg) and average labored breathing index (LBIavg) are both common, objective TAA measures—the first is a quantitative expression of the phase relation of TAA, and the second is a quantitative expression of the efficiency of respiratory movement and is indicative of the degree of TAA. See the supplemental material for complete mathematical definition of these terms. These objective measurements of TAA have been used in PSG and other contexts to quantify airway obstruction; however, there are few studies in the scientific literature to support use of these calculated values to identify OSA. Further evidence that either of these calculations help predict OSA could facilitate their future clinical use. This would also advance current understanding of OSA pathophysiology.

We sought to evaluate a simple quantitative method for predicting OSA in sleep studies using equipment and data already standardly used and collected during those studies. Specifically, we hypothesized that TAA measured by RIP, as represented by θavg during both stage N3 sleep and stage R sleep, positively correlates with OSA, as represented by obstructive apnea index (OAI). We prospectively collected and analyzed RIP data from our sleep studies in one of our largest and healthiest clinical groups and compared that analysis to the routine PSG report.

METHODS

This prospective observational study was performed at the American Academy of Sleep Medicine-accredited sleep laboratory at Nemours/Alfred I. duPont Hospital for Children and was approved by the institutional review board. In this study, RIP data collected during the hospital's sleep studies were quantitatively analyzed, and these calculations were compared to the clinical indices compiled by the clinical sleep physician. Forty-two otherwise healthy 3- to 8-year-old children who presented to the sleep laboratory for a scheduled PSG were recruited. These subjects reflect one of the largest pediatric clinical groups at risk for OSA: young children with difficulty sleeping, behavioral problems, snoring, and/or large tonsils. RIP was not expected to be specific enough for OSA in children younger than 3 years as they often have paradoxical breathing during sleep (and even while awake) as a part of normal physiology, secondary to immature respiratory mechanics.1517 To simplify the patient population and potential etiologies of OSA, we excluded older children and adolescents from the study. Children who might be at risk for central apnea or have abnormal breathing patterns or respiratory mechanics were also excluded. Specifically, they were excluded if they had underlying neuromuscular disease, skeletal dysplasia, genetic disorders, symptomatic congenital heart disease, uncontrolled asthma, chronic lung disease, tracheostomy, history of sternotomy, or they concurrently used positive airway pressure devices. All patients fitting the selection criteria who presented to the sleep laboratory on select nights during the time period of September 7, 2014 to December 9, 2014 were recruited. Parental consent was obtained and child assent was obtained for patients aged 7 to 8 years.

To maintain high-quality, uncalibrated RIP data during each PSG, standard positioning of the RIP bands on the chest at the level of the nipple and around the abdomen near the umbilicus was ensured. The principal investigator directly observed band placement and the resultant waveforms during the first half of each study. The Pro-Tech zRIP DuraBelt (Philips Respironics, Murrysville, Pennsylvania, United States) was used for RIP in these studies, connected via the Alice 5 Diagnostic Sleep System (Philips Respironics) and captured via Sleepware G3 diagnostic software, version 3.9.2 (Philips Respironics). The PSG tests also included recordings of electroencephalography, electrooculography, electromyography, and electrocardiography with heart rate, thermistor airflow, nasal pressure, capnography, pulse oximetry, audio, and video. The patients' sleep and associated events were recorded and scored by a registered polysomnographic technologist according to recommended standard criteria.10 Each PSG raw data file in European data format (EDF) was imported into a physiological analysis software program, VivoSense (Vivonoetics, San Diego, California, United States). RIP data from the EDF file was used to calculate θavg and LBIavg for the earliest 5-minute intervals of stage N3 sleep and stage R sleep that met specific acceptability criteria. Figure 1 provides an example of how to calculate θavg from the phase angles of a series of breaths. History and physical examination information from the pre-PSG clinic visit and PSG indices as calculated by board-certified sleep physicians were both obtained from the electronic medical record.

Example of paradoxical motion during stage R sleep and calculation of θavg.

Red x = idealized signal peak. θavg = average phase angle = sum of phase angles of each breath / total number of breaths (eg, average phase angle = [4 + 30 + 180 + 180 + 180 + 160 + 180 + 160 + 180] / 9 = 139).

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Figure 1

Example of paradoxical motion during stage R sleep and calculation of θavg.

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Even with direct observation and troubleshooting, RIP tracings are not always consistently acceptable for quantitative assessment; therefore, the following acceptability criteria were applied to ensure adequate, unbiased selection of the earliest usable continuous 5-minute intervals of stage N3 sleep and stage R sleep: (1) over the interval, there was concordance between the abdominal and thoracic tracings as to the number of breaths with a discrepancy of no more than 15%; (2) respiratory rate was 6 to 50 breaths per minute; and (3) the signal was at least 3 times the amplitude of the noise for at least 80% of the breaths.

The periods of stage N3 sleep and stage R sleep used for analysis were identified in a manner blind to PSG results or respiratory data by reviewing the raw PSG channels via a sleep-staging workspace. These intervals were labeled as stage N3 sleep or stage R sleep and identified by the time they started and ended. The investigator solely viewed RIP data from these time intervals in the absence of other PSG channels and did not view or collect PSG results (including indices or counts of events) until after automated calculation of TAA values by the analysis software. The PSG results were confirmed and finalized by clinical sleep physicians independently from this project, and prior to viewing by the principal investigator.

Statistical Methods

Summary statistics including mean and standard deviation (SD), median and interquartile range (IQR), and counts and proportions were calculated to describe the sample under study. The distribution of θavg and LBIavg during stage N3 sleep and stage R sleep between low (< 1) and high (≥ 1) OAI were compared using Mann-Whitney U test. Spearman correlation coefficients were calculated to quantify the association between OSA indices (as continuous variables) and θavg and LBIavg in each phase of sleep, as well as the relationship with body mass index (BMI) percentile. Wilcoxon rank-sum test was used to compare θavg/LBIavg in stage N3 sleep and stage R sleep. Data analysis was performed using R Statistical Software, version 3.1.3 (R Foundation for Statistical Computing, Vienna, Austria).

RESULTS

Forty-four patients were approached for the study. We recruited 42 patients and were able to analyze 40 of their PSG tests. For one PSG, one of the RIP bands malfunctioned and was unable to be corrected despite intervention from the investigator. Another study was terminated prior to onset of sleep and therefore had no sleep data for analysis. Only 5 studies required adjustment of the RIP bands after sleep onset, all within the first 2 hours.

Forty patients undergoing PSG were analyzed. Table 1 summarizes the demographic and clinical characteristics of these children. Figure 1 illustrates an example of paradoxical motion during stage R sleep and calculation of θavg. Average age was 5.7 years (range, 3.1–8.6). Fifty-five percent of patients were male. Thirty percent of patients were black. Thirty percent of patients had an official diagnosis of OSA from the clinical sleep physician, 57.5% had 3+/4+ enlarged tonsils, and 17.5% were obese with a BMI above the 95th percentile. Children with high and low OAI were not statistically significantly different with regard to demographic variables and clinical presentation (Table S1 in the supplemental material). Table 2 summarizes the PSG characteristics of the sample population.

Demographic and clinical characteristics of 40 children undergoing overnight polysomnography.

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Table 1

Demographic and clinical characteristics of 40 children undergoing overnight polysomnography.

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Polysomnographic characteristics of sample population.

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Table 2

Polysomnographic characteristics of sample population.

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Both θavg during stage N3 sleep and stage R sleep had significant positive correlations with OAI (Spearman r = .35 [P = .03] and 0.40 [P = .01], respectively). Figure 2 shows a box plot of OAI versus stage R sleep θavg to further demonstrate the relationship between these values. OAI was log transformed (with base e) for visualization; ln(OAI+1) was plotted. Similar significant results are seen when comparing θavg during stage N3 sleep (but not during stage R sleep) with obstructive apneahypopnea index (OAHI) (r = .35 [P = .03]). LBIavg during stage N3 sleep and stage R sleep had lower correlations. Table 3 summarizes the statistical comparisons between measures of TAA and OSA indices. Stage R sleep LBIavg has very strong correlations with stage R sleep OAI, stage R sleep OAHI, and stage R sleep apnea-hypopnea index (AHI) (r = .49 [P = .001], r = .44 [P = 0.005], and 0.48 [P = .002], respectively).

Box plot of OAI versus stage R sleep θavg group.

OAI was log transformed (with base e) for visualization. One was added to OAI and the natural log of that expression was plotted. Dots represent outliers. θavg = average phase angle, OAI = obstructive apnea index.

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Figure 2

Box plot of OAI versus stage R sleep θavg group.

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Relationship between measures of thoracoabdominal asynchrony and obstructive sleep apnea indices.

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Table 3

Relationship between measures of thoracoabdominal asynchrony and obstructive sleep apnea indices.

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Median θavg during stage R sleep (33.1) was significantly greater than during stage N3 sleep (13.7, P = .0005). Median LBIavg during stage R sleep (1.08) was significantly greater than during stage N3 sleep (1.03, P = .01). Figure 3 and Figure 4 show the difference in θavg and LBIavg, respectively, between stage R sleep and stage N3 sleep for each patient.

θavg in stage R sleep minus θavg in stage N3 sleep for each patient, in descending order.

θavg = average phase angle.

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Figure 3

θavg in stage R sleep minus θavg in stage N3 sleep for each patient, in descending order.

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LBIavg in stage R sleep minus LBIavg in stage N3 sleep for each patient, in descending order.

LBIavg = average labored breathing index.

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Figure 4

LBIavg in stage R sleep minus LBIavg in stage N3 sleep for each patient, in descending order.

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BMI percentile had no significant association with either OSA indices or measures of TAA, though the correlations with θavg and LBIavg seemed somewhat higher during stage R sleep than during stage N3 sleep. Table 4 summarizes this analysis of body habitus.

Relationship of BMI percentile with obstructive sleep apnea indices and measures of thoracoabdominal asynchrony.

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Table 4

Relationship of BMI percentile with obstructive sleep apnea indices and measures of thoracoabdominal asynchrony.

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DISCUSSION

The American Thoracic Society acknowledges that most work with RIP has been done in adults and infants, and only a few studies have otherwise been done in children.13 The adult literature shows some TAA in patients with chronic obstructive pulmonary disease and, specifically, increased θavg in patients with cystic fibrosis.1820 Infant studies have used summation of thoracic and abdominal RIP tracings to identify apneas.21,22 Other studies in infants have shown θavg to correlate with compliance and resistance after albuterol in bronchopulmonary dysplasia (BPD) and bronchiolitis,23 to correlate with presence of BPD,24 to correlate with airflow in methacholine challenge,25 and to correlate with history of premature gestation.26 One study has reported normative RIP values: in 3- to 5-year-old children, θavg of 15.7° while sitting, 29.8° while standing, and 56.1° while supine and LBIavg of 1.01 while sitting.27 Two studies tested use of RIP in more creative ways in older children. One evaluated breathing at home in ambulatory patients with Duchenne muscular dystrophy.28 Another examined RIP as a possible clinical decision tool to predict admission of patients with asthma exacerbation from the emergency room.29

Though a couple of studies have investigated quantitative use of RIP in infant sleep, only one looked at sleep in older children. In those studies, LBIavg was analyzed and found to predict OSA.16,30 One of the infant studies looking at short daytime naps found that θavg is reliable for quantifying TAA in prematurity.17 In the other study of diverse infants and toddlers undergoing daytime PSG, θavg predicted an abnormal PSG.31 The authors used a conservative definition of abnormal PSG— at least one episode of apnea, hypoxia, or hypoventilation— and a conservative cutoff for θavg, 24°. They concluded that RIP for θavg could potentially be used as screening test prior to PSG. Our study adds to the current literature by examining use of θavg and LBIavg in pediatric sleep studies of the most commonly seen group: children with symptoms of sleep-disordered breathing who are otherwise healthy.

Even in this group of pediatric patients with common clinical characteristics, we found evidence that θavg measured during PSG is associated with presence of OSA. RIP is a reliable tool in PSG, producing interpretable data consistently and only rarely requiring intervention to improve the signal quality after onset of sleep, as seen in our study. In our experience, RIP is one of the respiratory measurement devices best tolerated by young children during PSG, making it an important component of the study. θavg and LBIavg can be calculated automatically and do not require review or interpretation of the entire PSG. With our methodology, only 5 minutes of acceptable RIP data is required, generally obtainable from the first couple hours of sleep. Quantitative information gleaned from RIP could potentially improve the efficiency of PSG.

Prior studies have demonstrated that degree of airflow obstruction is a major determinant of θavg.23,24,32 Hypopneas involve much wider variation in airflow obstruction as compared to apneas. Obstruction in hypopneas reduces airflow 30% to 90% from baseline, whereas obstruction in apneas reduces airflow between 90% and 100%.10 However, when hypopneas are tallied in an OAHI they are weighed equally to apneas despite the differences in degree and variation of obstruction. This may explain differences between OAI and OAHI in the degree of association with TAA measures in this study. The TAA measures appear to be less sensitive for picking up hypopneas (during stage R sleep at least).

Though correlation of θavg with OAI and OAHI is significant, it is still low enough for speculation to remain as to other determinants of θavg. RIP belts slip as children change their position during sleep, and without vigilant monitoring and readjustment, this can affect the accuracy of TAA measures. It is possible RIP quality may have decreased later in each PSG after the principal investigator left the laboratory, and θavg may have become less accurate as a result. Sleep position may exert a large effect on θavg. Although not rigorously analyzed in our study, changes in position anecdotally seemed to co-occur with changes in θavg. In prior studies, other investigators have found that children's position (whether awake or asleep) significantly influences TAA.27,33 Belt slippage and/or mechanical positional changes could be implicated.

Body habitus does not appear to be a confounder of the relationship between TAA and OSA in this population, as BMI percentile does not correlate with OSA indices (Table 4). This may be the case in the study population due to the young age of the patients, as obesity is not the main cause of OSA in young children. This study, however, does not give any indication as to whether body habitus could be a confounder in an older or more obese population. BMI percentile may contribute somewhat here to TAA during stage R sleep (though this was not statistically significant, Table 4). It would seem that excess weight in our study population did not impair respiratory mechanics during non-rapid eye movement sleep (NREM), but with the paralysis of the accessory respiratory and intercostal muscles during stage R sleep excess weight may be able to effect impairment.

Although LBIavg did not correlate as well as θavg with OSA indices in our study, others have shown that LBIavg correlates very strongly with OAI in children with OSA when using qualitative diagnostic calibration (QDC) of the RIP for 5 minutes prior to sleep.30 LBIavg appears to be unaffected by change in body position when using QDC.26 We did not change our standard PSG practice to include QDC; however, this may be worth considering for future studies. The advantage of our decision not to use QDC is that we were able to determine the viability of quantitative measures of TAA for PSG as previously reported23,28,29 without change in practice or additional cost or effort.

Although prior studies have shown LBIavg to be higher in REM sleep than in NREM sleep, we now show θavg to be higher in stage R sleep as well.30 The significant difference between θavg in stage N3 sleep and stage R sleep confirms the observation that there is more TAA during REM sleep than during NREM sleep. This is consistent with REM-related paralysis of the genioglossus, pharyngeal, and intercostal muscles, causing upper airway obstruction with paradoxical inward rib cage movement occurring during outward abdominal movement.

Children with OSA may have much more TAA than non-OSA children during REM sleep, but only slightly more TAA than them during NREM sleep. This is suggested by our findings (Table S2 in the supplemental material). There is some protection from airway obstruction and OSA during NREM sleep, so TAA present at this time may not reflect airway obstruction to as great a degree. Also, there appears to be a smaller difference in TAA between OSA and non-OSA children during NREM sleep (Table S2). OSA is worse in REM sleep,34 and therefore measurements of TAA taken in NREM sleep may not correlate as well with sleep indices that account for both periods but disproportionately reflect the influence of REM sleep. AHI during stage R sleep is typically higher than total AHI—more than twice as high in this study, similar to findings in prior literature34 (Table 2).

It is possible that TAA during one stage of sleep could reflect obstruction occurring specifically during that stage. That appears to be the case during stage R sleep, as stage R sleep LBIavg has very strong correlations with stage R sleep OAI, stage R sleep OAHI, and stage R sleep AHI (Table 3).

It is important to note that this study is limited by a population consisting only of referred patients with symptoms of sleep-disordered breathing. Our findings cannot necessarily be extrapolated to a general population of children who may not have symptoms.

Association of θavg in stage N3 sleep and stage R sleep with OAI and OAHI may signify that although θavg is a marker of synchrony, it reflects airway obstruction and could have potential use as a quantitative indicator of OSA in combination with other established indicators. More work is required to determine how RIP can best produce even stronger OSA predictors in this population.

This study adds to the existing literature by extensively investigating the physiology of and quantitative measures of TAA in a group of typical pediatric sleep laboratory patients. This study provides evidence that θavg in NREM sleep and REM sleep correlate with indices of OSA (Table 3). This study, for the first time, quantifies the difference in TAA between NREM and REM sleep. This study provides evidence that BMI does not significantly influence TAA during sleep in young children (Table 4). This study describes reference ranges for TAA values by sleep stage in children with and in children without airway obstruction (Table S2). This study confirms θavg as a feasible measure in pediatric PSG.

DISCLOSURE STATEMENT

Work for this study was performed at Nemours/Alfred I. duPont Hospital for Children, Wilmington, DE. Support was received from the Nemours Foundation and an Institutional Development Award (IDeA) from the National Institute of General Medical Sciences of the National Institutes of Health under grant number NIH COBRE P30GM114736 (PI: Thomas H. Shaffer). All authors have seen and approved this manuscript. The authors report no conflicts of interest.

ABBREVIATIONS

θavg

average phase angle

AHI

apnea-hypopnea index

BMI

body mass index

BPD

bronchopulmonary dysplasia

EDF

European data format

IQR

interquartile range

LBIavg

average labored breathing index

NREM

non-rapid eye movement

OAHI

obstructive apnea-hypopnea index

OAI

obstructive apnea index

OSA

obstructive sleep apnea

PSG

polysomnography

QDC

qualitative diagnostic calibration

REM

rapid eye movement

RIP

respiratory inductance plethysmography

SD

standard deviation

TAA

thoracoabdominal asynchrony

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