Non-Invasive detection of respiratory effort-related arousals (REras) by a nasal cannula/pressure transducer system

Flow Limitation Is Associated with Excessive Daytime Sleepiness in Individuals without Moderate or Severe Obstructive Sleep Apnea

Rationale: Moderate-severe obstructive sleep apnea (OSA) (apnea-hypopnea index [AHI], >15 events/h) disturbs sleep through frequent bouts of apnea and is associated with daytime sleepiness. However, many individuals without moderate-severe OSA (i.e., AHI <15 events/h) also report sleepiness. Objectives: To test the hypothesis that sleepiness in the AHI <15 events/h group is a consequence of substantial flow limitation in the absence of overt reductions in airflow (i.e., apnea/hypopnea). Methods: A total of 1,886 participants from the MESA sleep cohort were analyzed for frequency of flow limitation from polysomnogram-recorded nasal airflow signal. Excessive daytime sleepiness (EDS) was defined by an Epworth Sleepiness Scale score ⩾11. Covariate-adjusted logistic regression assessed the association between EDS (binary dependent variable) and frequency of flow limitation (continuous) in individuals with an AHI <15 events/h. Results: A total of 772 individuals with an AHI <15 events/h were included in the primary analysis. Flow limitation was associated with EDS (odds ratio, 2.04; 95% confidence interval, 1.17-3.54; per 2-standard deviation increase in flow limitation frequency) after adjusting for age, sex, body mass index, race/ethnicity, and sleep duration. This effect size did not appreciably change after also adjusting for AHI. Conclusions: In individuals with an AHI <15 events/h, increasing flow limitation frequency by 2 standard deviations is associated with a twofold increase in the risk of EDS. Future studies should investigate addressing flow limitation in low-AHI individuals as a potential mechanism for ameliorating sleepiness.

Nasal cannula use during polysomnography in children aged under three with suspected sleep apnea

OBJECTIVE

Early diagnosis of obstructive sleep apnea (OSA) in children is important. The use of a nasal cannula as an airflow sensor during polysomnography has not been evaluated in younger children. The study aims to evaluate the use of nasal cannula in detecting respiratory events in children under three with suspected OSA during daytime nap studies.

METHODS

A total of 185 patients were prospectively included. Respiratory events were scored using nasal cannula alone, thermistor alone, and both methods simultaneously as the airflow sensor. Agreement and diagnostic accuracy were assessed.

RESULTS

One hundred and seventy-two children were finally analyzed and 110 (64.0%) presented OSA. Total sleep time with an uninterpretable signal was longer with the nasal cannula than with the thermistor (17.8% vs 1.9%; p < 0.001), and was associated with poor sensor tolerance and adenotonsillar hypertrophy. In the estimation of the apnea-hypopnea index, the nasal cannula showed lower agreement than the thermistor with the joint use of the two sensors (intraclass correlation coefficient: 0.79 vs 0.996 with thermistor). Compared with the thermistor, the nasal cannula presented lower sensitivity for detecting OSA (82.7% vs 95.5%) and a lower negative predictive value (76.5% vs 92.4%). Overall, fewer children were diagnosed with severe OSA with the nasal cannula (19.8% vs 30.8% with the thermistor, and 32.6% with both).

CONCLUSIONS

In children under the age of three, the ability of the nasal cannula to detect obstructive events was relatively low. Therefore, other non-invasive measurements for identifying respiratory events during sleep may be of additional value.

Quality of life in upper airway resistance syndrome

STUDY OBJECTIVES

To evaluate the health-related quality of life (HRQoL) in patients with upper airway respiratory syndrome (UARS) and obstructive sleep apnea (OSA) compared to the general population (GP) in Lima, Peru, and to explore the variables associated with differences in HRQoL in patients with UARS.

METHODS

This was a retrospective study of medical and polysomnography records from 2009-2014 in a referral sleep medicine center for patients aged 18-64 years. UARS was defined by polysomnography as follows: apnea-hypopnea index < 5 events/h, oxygen saturation ≥ 92%, respiratory effort-related arousal index ≥ 5. HRQoL was assessed using the 36-Item Short Form Survey (version 1) questionnaire validated in Peru. The GP HRQoL was obtained from a population-based survey. Linear and logistic regression analyses were conducted.

RESULTS

We reviewed 1,329 polysomnograms and selected 888. UARS and OSA were diagnosed in 93 and 795 participants, respectively. The GP cohort consisted of 641 participants. Total HRQoL mean scores (95% confidence interval) in patients with UARS, patients with OSA, and the GP were 67.4 (63.7-71.1), 66.9 (65.4-68.4), and 82.9 (81.6-84.3), respectively. Patients with UARS and patients with OSA had a 5.5 times (95% confidence interval, 3.3-9.2) and 6.2 times (95% confidence interval, 4.6-8.4) greater probability of having a low total HRQoL score compared to patients in the GP, respectively. In patients with UARS, muscle pain, use of psychotropic medication, obesity, and depression were negatively correlated with the total HRQoL score.

CONCLUSIONS

The impact of OSA and UARS on HRQoL is similar between disease groups and markedly worse when compared to the impact in the GP. In patients with UARS, the presence of muscle pain, obesity, female sex, depression, and use of psychotropic medication negatively impacted HRQoL.

CITATION

Vizcarra-Escobar D, Duque KR, Barbagelata-Agüero F, Vizcarra JA. Quality of life in upper airway resistance syndrome. J Clin Sleep Med. 2022;18(5):1263-1270.

Immediate Physiological Responses to Inspiratory Flow Limited Events in Mild Obstructive Sleep Apnea

Rationale: Inspiratory flow limitation (IFL), characterized by flattening of individual breaths on the airflow/time tracing, is a noninvasive indicator of elevated upper airway resistance. An IFL "event" in isolation has not been defined, nor has the ability to reproducibly identify event occurrence been tested. IFL events and their association with immediate physiological responses-as well as the impact of characteristics such as age, sex, sleep stage, sleepiness, and event duration on their association with such outcomes-have not been studied. Symptomatic patients with a normal to mildly abnormal apnea-hypopnea index who have predominant IFL on their polysomnography may benefit from treatment. Objectives: To test the reproducibility of identifying IFL events and their termination and to determine the frequency of the immediate physiological response to their occurrence, including desaturation, electroencephalography (EEG) arousal, and increased heart rate (HR). Methods: Fifty-eight patients with obstructive sleep apnea (OSA) underwent full diagnostic polysomnography. IFL events and their termination were identified manually using predefined rules from the unscored nasal cannula flow channel alone and were evaluated for responses such as EEG arousal, oxygen desaturation of ⩾3%, and HR increase. Results: Interscorer reliability was acceptable, with an average percent agreement for occurrence of 82% ± 3%. Of all IFL events, 24% (regardless of the definition) were not associated with an EEG arousal, an increase in HR, or O₂ desaturation. Of all IFL events scored, 25% caused O₂ desaturation, 40% were associated with an EEG arousal, and 55% were associated with an increase in HR; 67% caused either an EEG arousal and/or an increase in HR. Responses were observed to occur either in isolation or in combination. IFL events that terminated with at least two non-IFL breaths, one of which had a 200% increase in amplitude, were significantly associated with O₂ desaturation, EEG arousal, and increase in HR compared with events that ended in one non-IFL breath. IFL events that had a >50% reduction in flow amplitude compared with baseline were significantly associated with O₂ desaturation compared with events that had a 30% reduction or less. Conclusions: Most IFL events resulted in immediate physiological responses, and no single consequence reliably occurred after every event. We propose a framework that can incorporate the scoring of IFL events into assessing the diagnosis and severity of OSA and suggest that no single consequence be used to define IFL as a respiratory event. The relationship of IFL events to OSA outcomes remains to be tested.

Automatic detection of respiratory events during sleep from Polysomnography data using Layered Hidden Markov Model

OBJECTIVE

Sleep apnea is a serious respiratory disorder, which is associated with increased risk factors for cardiovascular disease. Many studies in recent years have been focused on automatic detection of sleep apnea from polysomnography (PSG) recordings, however, detection of subtle respiratory events named Respiratory Event Related Arousals (RERAs) that do not meet the criteria for apnea or hypopnea is still challenging. The objective of this study was to develop automatic detection of sleep apnea based on Hidden Markov Models (HMMs) which are probabilistic models with the ability to learn different dynamics of the real time-series such as clinical recordings.

APPROACH

In this study, a hierarchy of HMMs named Layered HMM was presented to detect respiratory events from PSG recordings. The recordings of 210 PSGs from Massachusetts General Hospital's database were used for this study. To develop detection algorithms, extracted feature signals from airflow, movements over the chest and abdomen, and oxygen saturation in blood (SaO2) were chosen as observations. The respiratory disturbance index (RDI) was estimated as the number of apneas, hypopneas, and RERAs per hour of sleep.

MAIN RESULTS

The best F1 score of the event by event detection algorithm was between 0.22±0.16 and 0.70±0.08 for different groups of sleep apnea severity. There was a strong correlation between the estimated and the PSG-derived RDI (R2=0.91, p<0.0001). The best recall of RERA detection was achieved 0.45±0.27.

SIGNIFICANCE

The results showed that the layered structure can improve the performance of the detection of respiratory events during sleep.

Tidal spirometric curves obtained from a nasal cannula

Spirometry is a gold standard to assess lung function, and to identify respiratory impairments seen in obstructive lung diseases. The method is used for periodic monitoring, but it only provides snapshot information, and it requires forceful exhalation which is associated with limited reliability and repeatability. Several studies indicate that tidal flow-volume curves measured by pneumotachography or plethysmography can also be used to assess lung function. These methods avoid the forced manoeuvre, but are complex to set up or sensitive to movement. In the present work we address the long-standing problem of the unavailability of an easy-to-use and accurate method for monitoring tidal breathing frequently or even continuously. We show that pressure recordings from a nasal cannula can be accurately converted into scaled flow-volume curves by means of an algorithm that continuously calibrates itself. The method has been validated by feeding realistic healthy and unhealthy breathing patterns to anatomically correct 3D-printed upper airways of an infant and an adult, and by comparing the imposed flow-volume curves to the pressure-derived flow-volume curves. The observed very high level of accuracy opens the route towards remotely monitoring patients with chronic lung diseases.

Artificial intelligence and sleep: Advancing sleep medicine

Artificial intelligence (AI) allows analysis of "big data" combining clinical, environmental and laboratory based objective measures to allow a deeper understanding of sleep and sleep disorders. This development has the potential to transform sleep medicine in coming years to the betterment of patient care and our collective understanding of human sleep. This review addresses the current state of the field starting with a broad definition of the various components and analytic methods deployed in AI. We review examples of AI use in screening, endotyping, diagnosing, and treating sleep disorders and place this in the context of precision/personalized sleep medicine. We explore the opportunities for AI to both facilitate and extend providers' clinical impact and present ethical considerations regarding AI derived prognostic information. We cover early adopting specialties of AI in the clinical realm, such as radiology and pathology, to provide a road map for the challenges sleep medicine is likely to face when deploying this technology. Finally, we discuss pitfalls to ensure clinical AI implementation proceeds in the safest and most effective manner possible.

Home sleep apnea testing: an accuracy study

AIM

There are no studies comparing tests performed at home with those carried out in the laboratory, using the same device. The only studies that have been performed have compared the device used at home with the standard polygraph used in the laboratory. The purpose of this study was therefore to verify the accuracy of the home diagnosis of obstructive sleep apnea syndrome (OSAS) via unassisted type 2 portable polysomnography, compared with polysomnography using the same equipment in a sleep laboratory.

METHODS

To avoid any possible order effect on the apnea-hypopnea index (AHI), we randomly created two groups of 20-total 40 patients, according to the test sequence. One of the groups had the first test at home and the second test in the laboratory (H-L); the other group had the first test in the laboratory and the second at home (L-H). The second test always took place on the night immediately following the first test. All polysomnographic monitoring was undertaken with the same equipment, an Embletta X100 system (Embla, Natus Inc., Middleton, USA). The Embletta X100 is a portable polygraph that records eleven polygraph signs: (1) electroencephalogram C4/A; (2) electroencephalogram O2/M1; (3) submental EMG; (4) electrooculogram of the right side; (5) nasal cannula (air flow); (6) respiratory effort against a plethysmographic chest strap; (7) respiratory effort against an abdominal plethysmographic belt; (8) heart rate; (9) saturation of oxyhemoglobin; (10) snoring; and (11) body position.

RESULTS

There was no difference in sleep efficiency between the group monitored in the laboratory and the group tested at home (p = 0.30). There was no difference in total sleep time (p = 0.11) or sleep latency (p = 0.52), or in the latency in phases N2 and N3 between the monitoring in the laboratory and at home (N2 p = 0.24; N3 p = 0.09). Some differences occurred regarding the PSG that took place at home, with longer duration of wake after sleep onset (WASO) and longer latency for REM sleep, due to failure of the patient to start the monitoring by pressing the "events" button on the device. In the distribution of sleep phases, there was no difference between the group monitored in the laboratory and the group tested at home.

CONCLUSION

Results from home sleep monitoring correlate well with the laboratory "gold standard" and may be an option for diagnosing OSAS in selected patients.

Verification of the Propagation Range of Respiratory Strain Using Signal Waveform Measured by FBG Sensors

The FBG (Fiber Bragg grating) sensor is an optical fiber type strain sensor. When a person breathes, strain occurs in the lungs and diaphragm. This was verified using an FBG sensor to which part of the living body this respiratory strain propagates. When measured in the abdomen, the signal waveforms were significantly different between breathing and apnea. The respiratory cycle measured by the temperature sensor attached to the mask and the strain cycle measured by the FBG sensor almost matched. Respiratory strain was measured in the abdomen, chest, and shoulder, and the signal amplitude decreased with distance from the abdomen. In addition, the respiratory rate could be calculated from the measured strain signal. On the other hand, respiratory strain did not propagate to the elbows and wrists, which were off the trunk, and the respiratory time, based on the signal period, could not be calculated at these parts. Therefore, it was shown that respiratory strain propagated in the trunk from the abdomen to the shoulder, but not in the peripheral parts of the elbow and wrist.

Machine-learning enabled wireless wearable sensors to study individuality of respiratory behaviors

Respiratory behaviors provide useful measures of lung health. The current methods have limited capabilities of continuous characterization of respiratory behaviors, often required to assess respiratory disorders and diseases. This work presents a system equipped with a machine learning algorithm, capable of continuously monitoring respiratory behaviors. The system, consisting of two wireless wearable sensors, accurately extracts and classifies the features of respiratory behaviors of subjects within various postures, wirelessly transmitting the temporal respiratory behaviors to a laptop. The sensors were attached on the midway of the xiphoid process and the costal margin, and 1 cm above the umbilicus, respectively. The wireless wearable sensor, consisting of ultrasound emitter, ultrasound receiver, data acquisition and wireless transmitter, has a small footprint and light weight. The sensors correlate the mechanical strain at wearing sites to lung volume by measuring the local circumference changes of the chest and abdominal walls simultaneously. Eleven subjects were recruited to evaluate the wireless wearable sensors. Three different random forest classifiers, including generic, individual, and weighted-adaptive classifiers, were used to process the wireless data of the subjects at four different postures. The results demonstrate the respiratory behaviors are individual- and posture-dependent. The generic classifier merely reaches the accuracy of classifying postures of 21.9 ± 1.7% while individual and weighted-adaptive classifiers mark substantially high, up to 98.9 ± 0.6% and 98.8 ± 0.6%, respectively. The accurate monitoring of respiratory behaviors can track the progression of respiratory disorders and diseases, including chronic respiratory obstructive disease (COPD), asthma, apnea, and others for timely and objective approaches for control.

Normal and obstructive breathing physiology during sleep

PURPOSE

To estimate the severity of flow limitation in patients with OSA, the number of breaths with flattened inspiratory flow curves should be identified. Attempts to do a quantitative analysis of the flattening degree for all breaths in a nighttime recording have failed up to now.

METHODS

SOMNOmedics (Randersacker, Germany) developed an automated flattening analysis parameter called the obstructive coefficient (OC). Polysomnographic measurement including esophageal manometry was done in 25 subjects (10 healthy, 9 patients with mild OSA, and 6 with severe OSA). For each breath, the data couple of OC and esophageal pressure (EP) was used for analysis.

RESULTS

Data couples of OC and EP were recorded for 104,608 breaths. Airway patency histogram profiles for each study group showed no remarkable differences between each other. Increase in EP with increasing RDI was identified as the only marker of OSA severity. A strong shift was observed in N3 breaths from maximum OC/lowest EP values in healthy subjects to low OC values in association with maximum EP values in OSA.

CONCLUSION

The OC enables quantification of all breaths of a nighttime recording according to their degree of flattening. The relation of strong limited to less strong limited breaths is the same across the three study groups. The analysis of the corresponding EP to a given OC value for each study group identified the EP that is necessary to cause a given flow as the only parameter that discriminates degrees of severity of OSA. The trial registration number is DRKS00018095 from 2019 to 10-09.

Sleep Studies Interpretation and Application

Obstructive sleep apnea is a common, but under-recognized, condition. Polysomnography remains the gold standard for diagnosing obstructive sleep apnea and determining whether treatment is appropriate. The development of home sleep apnea testing has allowed for a faster and more convenient method of diagnosis. Continuous positive airway pressure is the therapy of choice for most patients with obstructive sleep apnea, but otorhinolaryngologists can expect to see more and more patients looking for alternative treatments. This review highlights salient points relevant to sleep study application and interpretation for otorhinolaryngologists.

Trend Statistics Network and Channel invariant EEG Network for sleep arousal study

Sleep is a very important part of life. Lack of sleep or sleep disorder can cause a negative impact on day to day life and can have long term serious consequences. In this work, we propose an end-to-end trainable neural network for automated sleep arousal scoring. The network consists of two main parts. Firstly, a trend statistics network computes the moving average of the filtered signals at different scales. Secondly, we propose a channel invariant EEG network to detect the arousals in any Electroencephalography (EEG) channel. Finally, we combine the features from various channels through a convolution network and a bi-directional long short-term memory to predict the probability of arousal. Further, we propose an objective function that uses only respiratory effort related arousal (RERA) and non-arousal regions to optimize the network. We also propose a method to estimate the respiratory disturbance index (RDI) from the probability predicted by the network. Evaluation on Physionet Challenge 2018 database shows that the proposed method detects RERA with mean area under the precision-recall curve (AUPRC) of 0.50 in a 10-fold cross validation setup. The mean absolute error of RDI prediction is 6.11, while a two-class RDI severity prediction yields a specificity of 75% and a sensitivity of 83%.

Validating an Algorithm for Automatic Scoring of Inspiratory Flow Limitation Within a Range of Recording Settings

Inspiratory Flow Limitation (IFL) is a phenomenon associated with narrowing of the upper airway, preventing an increase in inspiratory airflow despite an elevation in intrathoracic pressure. It has been shown that quantification of IFL might complement information provided by standard indices such as the apnea-hypopnea index (AHI) in characterizing sleep disordered breathing and identifying subclinical disease. Defining guidelines for visual scoring of IFL has been of increasing interest, and automated methods are desirable to avoid inter-scorer variability and allow analysis of large datasets. In addition, as recording instrumentation and practices may vary across hospitals and laboratories, it is useful to assess the influence of the recording parameters on the accuracy of the automated classification. We employed nasal pressure signals recorded as part of polysomnography (PSG) studies in 7 patients. Two experts independently classified approximately 2000 breaths per subject as IFL or non-IFL, and we used the consensus scoring as the gold standard. For each breath, we derived features indicative of the shape and frequency content of the signals and used them to train and validate a Support Vector Machine (SVM) to distinguish IFL from non-IFL breaths. We also assessed the effect of signal filtering (down-sampling and baseline-removal) on classification performance. The performance of the classifier was excellent (accuracy ~93%) for the raw signals (collected at 125 Hz with no filtering), and decreased for increasing high-pass cut-off frequencies (fc = [0.05, 0.1, 0.15, 0.2] Hz) down to 84% for fc= 0.2 Hz and for decreasing sampling rate (fs = [20, 50, 75, 100] Hz) down to ~85% for fs=20 Hz. Loss of performance was minimized when the classifier was re-trained using data with matched filtering characteristics (accuracy > 89%). We can conclude that the SVM feature-based algorithm provides a reliable and efficient tool for breath-by-breath classification.

Quantifying the magnitude of pharyngeal obstruction during sleep using airflow shape

Rationale and objectives

Non-invasive quantification of the severity of pharyngeal airflow obstruction would enable recognition of obstructiveversuscentral manifestation of sleep apnoea, and identification of symptomatic individuals with severe airflow obstruction despite a low apnoea–hypopnoea index (AHI). Here we provide a novel method that uses simple airflow-versus-time (“shape”) features from individual breaths on an overnight sleep study to automatically and non-invasively quantify the severity of airflow obstruction without oesophageal catheterisation.

Methods

41 individuals with suspected/diagnosed obstructive sleep apnoea (AHI range 0–91 events·h−1) underwent overnight polysomnography with gold-standard measures of airflow (oronasal pneumotach: “flow”) and ventilatory drive (calibrated intraoesophageal diaphragm electromyogram: “drive”). Obstruction severity was defined as a continuous variable (flow:drive ratio). Multivariable regression used airflow shape features (inspiratory/expiratory timing, flatness, scooping, fluttering) to estimate flow:drive ratio in 136 264 breaths (performance based on leave-one-patient-out cross-validation). Analysis was repeated using simultaneous nasal pressure recordings in a subset (n=17).

Results

Gold-standard obstruction severity (flow:drive ratio) varied widely across individuals independently of AHI. A multivariable model (25 features) estimated obstruction severity breath-by-breath (R2=0.58versusgold-standard, p<0.00001; mean absolute error 22%) and the median obstruction severity across individual patients (R2=0.69, p<0.00001; error 10%). Similar performance was achieved using nasal pressure.

Conclusions

The severity of pharyngeal obstruction can be quantified non-invasively using readily available airflow shape information. Our work overcomes a major hurdle necessary for the recognition and phenotyping of patients with obstructive sleep disordered breathing.

The Association between Health Conditions in World Trade Center Responders and Sleep-Related Quality of Life and Sleep Complaints

Background: World Trade Center (WTC) dust-exposed subjects have multiple comorbidities that affect sleep. These include obstructive sleep apnea (OSA), chronic rhinosinusitis (CRS), gastroesophageal-reflux disorder (GERD) and post-traumatic stress disorder (PTSD). We examined the impact of these conditions to sleep-related outcomes. Methods: Demographics, co-morbidities and symptoms were obtained from 626 WTC (109F/517M), 33⁻87years, BMI = 29.96 ± 5.53 kg/m²) subjects. OSA diagnosis was from a 2-night home sleep test (ARES^TM). Subjective sleep quality, sleep-related quality of life (QOL, Functional Outcomes of Sleep Questionnaire), excessive daytime sleepiness (Epworth Sleepiness Scale), sleep duration and sleep onset and maintenance complaints were assessed. Results: Poor sleep quality and complaints were reported by 19⁻70% of subjects and average sleep duration was 6.4 h. 74.8% of subjects had OSA. OSA diagnosis/severity was not associated with any sleep-related outcomes. Sleep duration was lower in subjects with all conditions (p < 0.05) except OSA. CRS was a significant risk factor for poor sleep-related QOL, sleepiness, sleep quality and insomnia; PTSD for poor sleep-related QOL and insomnia; GERD for poor sleep quality. These associations remained significant after adjustment for, age, BMI, gender, sleep duration and other comorbidities. Conclusions: Sleep complaints are common and related to several health conditions seen in WTC responders. Initial interventions in symptomatic patients with both OSA and comorbid conditions may need to be directed at sleep duration, insomnia or the comorbid condition itself, in combination with intervention for OSA.

Upper airway resistance syndrome 2018: non-hypoxic sleep-disordered breathing

INTRODUCTION

Upper airway resistance syndrome (UARS) as obstructive sleep apnea syndrome (OSAS) has been described as abnormal breathing during sleep, based on the recording technologies and knowledge of the time. These terms have advanced the field, but are they still useful? Area Covered: Historically, the definition of UARS syndrome was aimed at recognizing pathology not covered by 'OSAS' and to prompt specialists to go further than the obvious. It aimed at pushing specialists to recognize pathologies earlier and to elicit research in the developmental features of sleep-disordered-breathing (SDB). The technology used to monitor SDB changed over-time, allowing recognition of SDB differently but not necessarily better. Expert Commentary: Currently, we have a better understanding of the development of SDB, and its evolution with aging, leading to co-morbid-OSA. However, the real issue is to recognize the problems much earlier, and to understand what can be done to prevent its development. The notions of OSA, UARS, apnea hypopnea index are only historical. There is enough knowledge to date to go beyond these definitions, to recognize problems differently and to lead to the prevention of the factors leading to SDB. The recognition of non-hypoxic sleep-disordered breathing is a step in this direction.

Addition of frontal EEG to adult home sleep apnea testing: does a more accurate determination of sleep time make a difference?

RATIONALE

Home sleep apnea testing (HSAT) typically does not include electroencephalogram (EEG) monitoring for sleep assessment. In patients with insomnia and low sleep efficiency, overestimation of the sleep period can result from absence of EEG, which will reduce sleep disordered breathing (SDB) indices and may lead to a false-negative result.

OBJECTIVE

To validate a single channel frontal EEG for scoring sleep versus wake against full EEG during polysomnography, and then to examine the utility of adding this single channel EEG to standard HSAT to prevent false-negative results.

METHODS

Epoch-by-epoch validation for sleep scoring of single channel EEG versus full PSG was first performed in 21 subjects. This was followed by a separate retrospective analysis of 207 consecutive HSATs in adults performed in a university-affiliated sleep center using the Somte (Compumedics) HSAT with one frontal EEG as well as chin EMG, nasal airflow, oxyhemoglobin saturation, respiratory effort, pulse rate, and body position. Each study was scored twice, with (HSAT_EEG) and without the EEG signal visible (HSAT_Polygraphy), to calculate AHI4 and RDI and the effect on OSA diagnosis and severity. Analyses were repeated in 69 patients with poor sleep suggesting insomnia plus Epworth Sleepiness Scale < 7 as well as in 38 patients ultimately shown to have sleep efficiency < 70% on HSAT with EEG.

MEASUREMENTS AND MAIN RESULTS

Single channel and full EEG during polysomnography agreed on sleep versus wake in 92-95% of all epochs. HSAT without EEG overestimated the sleep period by 20% (VST = 440 ± 76 min vs TST = 356 ± 82 min), had a false-negative rate of 8% by AHI4 criteria, and underestimated disease severity in 11% of all patients. Sub-group analysis of patients with subjective poor sleep suggesting insomnia did not change the results. Patients later shown to have low sleep efficiency had lower SDB indices and a 20.8% false negative rate of sleep apnea diagnosis.

CONCLUSIONS

Although overall false negative rates using HSAT_Polygraphy were moderate, suggesting utility for ruling out OSA, there was a specific subgroup in whom there were significant missed diagnoses. However, we were unable to identify this subgroup a priori.

An Official American Thoracic Society Workshop Report: Noninvasive Identification of Inspiratory Flow Limitation in Sleep Studies

This report summarizes the proceedings of the American Thoracic Society Workshop on the Noninvasive Identification of Inspiratory Flow Limitation in Sleep Studies held on May 16, 2015, in Denver, Colorado. The goal of the workshop was to discuss methods for standardizing the scoring of flow limitation from nasal cannula pressure tracings. The workshop began with presentations on the physiology underlying flow limitation, existing methods of scoring flow limitation, the effects of signal acquisition and filtering on flow shapes, and a review of the literature examining the adverse outcomes related to flow limitation. After these presentations, the results from online scoring exercises, which were crowdsourced to workshop participants in advance of the workshop, were reviewed and discussed. Break-out sessions were then held to discuss potential algorithms for scoring flow limitation. Based on these discussions, subsequent online scoring exercises, and webinars after the workshop, a consensus-based set of recommendations for a scoring algorithm for flow limitation was developed. Key conclusions from the workshop were: (1) a standardized and automated approach to scoring flow limitation is needed to provide a metric of nonepisodic elevated upper airway resistance, which can then be related to clinical outcomes in large cohorts and patient groups; (2) at this time, the most feasible method for standardization is by proposing a consensus-based framework, which includes scoring rules, developed by experts (3) hardware and software settings of acquisition devices, including filter settings, affect the shape of the flow curve, and should be clearly specified; and (4) a priority for future research is the generation of an open-source, expert-derived training set to encourage and support validation of automated flow limitation scoring algorithms.

Sleep apnea: a review of diagnostic sensors, algorithms, and therapies

While public awareness of sleep related disorders is growing, sleep apnea syndrome (SAS) remains a public health and economic challenge. Over the last two decades, extensive controlled epidemiologic research has clarified the incidence, risk factors including the obesity epidemic, and global prevalence of obstructive sleep apnea (OSA), as well as establishing a growing body of literature linking OSA with cardiovascular morbidity, mortality, metabolic dysregulation, and neurocognitive impairment. The US Institute of Medicine Committee on Sleep Medicine estimates that 50-70 million US adults have sleep or wakefulness disorders. Furthermore, the American Academy of Sleep Medicine (AASM) estimates that more than 29 million US adults suffer from moderate to severe OSA, with an estimated 80% of those individuals living unaware and undiagnosed, contributing to more than $149.6 billion in healthcare and other costs in 2015. Although various devices have been used to measure physiological signals, detect apneic events, and help treat sleep apnea, significant opportunities remain to improve the quality, efficiency, and affordability of sleep apnea care. As our understanding of respiratory and neurophysiological signals and sleep apnea physiological mechanisms continues to grow, and our ability to detect and process biomedical signals improves, novel diagnostic and treatment modalities emerge.

OBJECTIVE

This article reviews the current engineering approaches for the detection and treatment of sleep apnea.

APPROACH

It discusses signal acquisition and processing, highlights the current nonsurgical and nonpharmacological treatments, and discusses potential new therapeutic approaches.

MAIN RESULTS

This work has led to an array of validated signal and sensor modalities for acquiring, storing and viewing sleep data; a broad class of computational and signal processing approaches to detect and classify SAS disease patterns; and a set of distinctive therapeutic technologies whose use cases span the continuum of disease severity.

SIGNIFICANCE

This review provides a current perspective of the classes of tools at hand, along with a sense of their relative strengths and areas for further improvement.

Mandibular Movements As Accurate Reporters of Respiratory Effort during Sleep: Validation against Diaphragmatic Electromyography

Context

Mandibular movements (MM) are considered as reliable reporters of respiratory effort (RE) during sleep and sleep disordered breathing (SDB), but MM accuracy has never been validated against the gold standard diaphragmatic electromyography (EMG-d).

Objectives

To assess the degree of agreement between MM and EMG-d signals during different sleep stages and abnormal respiratory events.

Methods

Twenty-five consecutive adult patients with SDB were studied by polysomnography (PSG) that also included multipair esophageal diaphragm electromyography and a magnetometer to record MM. EMG-d activity (microvolt) and MM (millimeter) amplitudes were extracted by envelope processing. Agreement between signals amplitudes was evaluated by mixed linear regression and cross-correlation function and in segments of PSG including event-free and SDB periods.

Results

The average total sleep time was 370 ± 18 min and the apnea hypopnea index was 24.8 ± 5.2 events/h. MM and EMG-d amplitudes were significantly cross-correlated: median r (95% CI): 0.67 (0.23–0.96). A mixed linear model showed that for each 10 µV of increase in EMG-d activity, MM amplitude increased by 0.28 mm. The variations in MM amplitudes (median range: 0.11–0.84 mm) between normal breathing, respiratory effort-related arousal, obstructive, mixed, and central apnea periods closely corresponded to those observed with EMG-d activity (median range: 2.11–8.23 µV).

Conclusion

MM amplitudes change proportionally to diaphragmatic EMG activity and accurately identify variations of RE during normal sleep and SDB.

The relationship between partial upper-airway obstruction and inter-breath transition period during sleep

Short pauses or "transition-periods" at the end of expiration and prior to subsequent inspiration are commonly observed during sleep in humans. However, the role of transition periods in regulating ventilation during physiological challenges such as partial airway obstruction (PAO) has not been investigated. Twenty-nine obstructive sleep apnea patients and eight controls underwent overnight polysomnography with an epiglottic catheter. Sustained-PAO segments (increased epiglottic pressure over ≥5 breaths without increased peak inspiratory flow) and unobstructed reference segments were manually scored during apnea-free non-REM sleep. Nasal pressure data was computationally segmented into inspiratory (T_I, shortest period achieving 95% inspiratory volume), expiratory (T_E, shortest period achieving 95% expiratory volume), and inter-breath transition period (T_Trans, period between T_E and subsequent T_I). Compared with reference segments, sustained-PAO segments had a mean relative reduction in T_Trans (-24.7±17.6%, P<0.001), elevated T_I (11.8±10.5%, P<0.001), and a small reduction in T_E (-3.9±8.0, P≤0.05). Compensatory increases in inspiratory period during PAO are primarily explained by reduced transition period and not by reduced expiratory period.

Sleep continuity is positively correlated with sleep duration in laboratory nighttime sleep recordings

Sleep duration varies widely across individuals and appears to be trait-like. Differences in the stability of underlying sleep processes may underlie this phenomenon. To investigate underlying mechanisms, we examined the relationship between sleep duration and sleep continuity in baseline polysomnography (PSG) recordings from three independently collected datasets: 1) 134 healthy controls (ages 37 ± 13 years) from the São Paulo Epidemiologic Sleep Study, who spent one night in a sleep laboratory, 2) 21 obstructive sleep apnea (OSA) patients who were treated with continuous positive airway pressure for at least 2 months (45 ± 12 years, respiratory disturbance index <15), who spent one night in a sleep laboratory with previous experience of multiple PSG studies, and 3) 62 healthy controls (28 ± 6 years) who, as part of larger experiments, spent 2 consecutive nights in a sleep laboratory. For each dataset, we used total sleep time (TST) to separate subjects into those with shorter sleep (S-TST) and those with longer sleep (L-TST). In all three datasets, survival curves of continuous sleep segments showed greater sleep continuity in L-TST than in S-TST. Correlation analyses with TST as a continuous variable corroborated the results; and the results also held true after controlling for age. There were no significant differences in baseline waking performance and sleepiness between S-TST and L-TST. In conclusion, in both healthy controls and treated OSA patients, sleep continuity was positively correlated with sleep duration. These findings suggest that S-TST may differ from L-TST in processes underlying sleep continuity, shedding new light on mechanisms underlying individual differences in sleep duration.

Respiratory effort from the photoplethysmogram

The potential for a simple, non-invasive measure of respiratory effort based on the pulse oximeter signal - the photoplethysmogram or 'pleth' - was investigated in a pilot study. Several parameters were developed based on a variety of manifestations of respiratory effort in the signal, including modulation changes in amplitude, baseline, frequency and pulse transit times, as well as distinct baseline signal shifts. Thirteen candidate parameters were investigated using data from healthy volunteers. Each volunteer underwent a series of controlled respiratory effort maneuvers at various set flow resistances and respiratory rates. Six oximeter probes were tested at various body sites. In all, over three thousand pleth-based effort-airway pressure (EP) curves were generated across the various airway constrictions, respiratory efforts, respiratory rates, subjects, probe sites, and the candidate parameters considered. Regression analysis was performed to determine the existence of positive monotonic relationships between the respiratory effort parameters and resulting airway pressures. Six of the candidate parameters investigated exhibited a distinct positive relationship (p<0.001 across all probes tested) with increasing upper airway pressure repeatable across the range of respiratory rates and flow constrictions studied. These were: the three fundamental modulations in amplitude (AM-Effort), baseline (BM-Effort) and respiratory sinus arrhythmia (RSA-Effort); two pulse transit time modulations - one using a pulse oximeter probe and an ECG (P2E-Effort) and the other using two pulse oximeter probes placed at different peripheral body sites (P2-Effort); and baseline shifts in heart rate, (BL-HR-Effort). In conclusion, a clear monotonic relationship was found between several pleth-based parameters and imposed respiratory loadings at the mouth across a range of respiratory rates and flow constrictions. The results suggest that the pleth may provide a measure of changing upper airway dynamics indicative of the effort to breathe.

Pulse transit time changes in subjects exhibiting sleep disordered breathing

INTRODUCTION

Pulse Transit Time (PTT) represents a non-invasive marker of sleep fragmentation in OSAS. Little is known regarding PTT in sleepy subjects exhibiting nocturnal Inspiratory Flow Limitation (IFL) in the absence of apneas or desaturation.

MATERIALS AND METHODS

The IFL cohort was gender and age matched to subjects with OSAS and a cohort where Sleep Disordered Breathing (SBD)/IFL was absent ("Non Flow Limited" or NFL cohort); PTT Arousal index (PTT Ar) defined by number of PTT arousals per hour.

RESULTS

20 subjects meeting criteria for the IFL cohort were aged and gender matched with OSAS and "NFL" subjects. Females comprised 65% of the IFL cohort; the mean BMI of the IFL cohort was significantly higher than the NFL cohort (34.25 v 28.90; p = 0.016) but not when compared to the OSAS cohort (34.25 v 36.31; p = 0.30). The PTT Ar in the IFL cohort (33.67 h) was significantly higher than the NFL cohort (23.89 h) but significantly lower than the OSAS cohort (55.21 h; F = 8.76; p < 0.001). PTT Ar was found to positively correlate with AHI (CC = 0.46; p < 0.001), ODI (CC = 0.47; p < 0.001) and RDI (CC = 0.49; p < 0.001). Within the IFL cohort, PTT Ar positively correlated with age (CC = 0.501; p = 0.024) but not gender and BMI.

CONCLUSION

The PTT Arousal Index increased proportionately with severity of SDB with significantly higher markers of arousal in sleepy subjects exhibiting nocturnal IFL when compared to controls. Subjects exhibiting IFL were predominantly female with an elevated BMI. IFL may thus represent a significant pathogenic entity in the development of daytime sleepiness.

How to Evaluate a Diagnostic Sleep Study Report

There is more information on a sleep study report than just the Apnea-Hypopnea Index or Respiratory Disturbance Index. This article explains how to evaluate any sleep study report to get the most information out of it. Maximum information allows the optimal treatment of patients with obstructive sleep apnea and some other sleep disorders.

New Rule-Based Algorithm for Real-Time Detecting Sleep Apnea and Hypopnea Events Using a Nasal Pressure Signal

We developed a rule-based algorithm for automatic real-time detection of sleep apnea and hypopnea events using a nasal pressure signal. Our basic premise was that the performance of our new algorithm using the nasal pressure signal would be comparable to that using other sensors as well as manual annotation labeled by a technician on polysomnography study. We investigated fifty patients with sleep apnea-hypopnea syndrome (age: 56.8 ± 10.5 years, apnea-hypopnea index (AHI): 36.2 ± 18.1/h) during full night PSG recordings at the sleep center. The algorithm was comprised of pre-processing with a median filter, amplitude computation and apnea-hypopnea detection parts. We evaluated the performance of the algorithm a confusion matric for each event and statistical analyses for AHI. Our evaluation achieved a good performance, with a sensitivity of 86.4 %, and a positive predictive value of 84.5 % for detection of apnea and hypopnea regardless of AHI severity. Our results indicated a high correlation with the manually labeled apnea-hypopnea events during PSG, with a correlation coefficient of r = 0.94 (p < 0.0001) and a mean difference of -2.9 ± 11.6 per hour. The proposed new algorithm could provide significant clinical and computational insights to design a PSG analysis system and a continuous positive airway pressure (CPAP) device for screening sleep quality related in patients with sleep apnea-hypopnea syndrome.

The effect of compound nasal surgery on obstructive sleep apnea syndrome

BACKGROUND

Nasal surgery often fails to ameliorate the symptoms of obstructive sleep apnea syndrome (OSAS). We developed a compound nasal surgery (CNS) method that consists of septoplasty combined with submucosal inferior turbinectomy and posterior nasal neurectomy to ensure low nasal resistance during sleep.

OBJECTIVE

To clarify the effect of CNS on OSAS, pre- and postoperative changes in sleep-related events were studied by using polysomnography, the Epworth sleepiness scale (ESS), the visual analog scale for snoring, and health-related quality of life (QOL).

METHODS

Forty-five consecutive patients with OSAS and with nasal problems underwent CNS. Three months later, the postoperative effect on OSAS was assessed by using polysomnography findings, daytime sleepiness by the ESS, nasal allergy symptoms, and health-related QOL. Snoring was assessed by the family by using a visual analog scale.

RESULTS

The indices of apnea, apnea-hypopnea, oxygen desaturation, and arousal; the ESS; allergic symptom score; health-related QOL; and snoring on a visual analog scale were all significantly improved.

CONCLUSIONS

CNS improves OSAS events without any pharyngeal surgical procedure in selected patients. If high nasal resistance associated with OSAS is present, then CNS should thus be considered.

Mild obstructive sleep apnoea: clinical relevance and approaches to management

Obstructive sleep apnoea is highly prevalent in the general population worldwide, especially in its mild form. Clinical manifestations correlate poorly with disease severity measured by the apnoea-hypopnoea index (AHI), which complicates diagnosis. Full polysomnography might be more appropriate to assess suspected mild cases because limited ambulatory diagnostic systems are least accurate in mild disease. Treatment options in mild obstructive sleep apnoea include continuous positive airway pressure (CPAP) and oral appliance therapy, in addition to positional therapy and weight reduction when appropriate. The superior efficacy of CPAP in reducing AHI is offset by greater tolerance of oral appliances, especially in mild disease. Although severe obstructive sleep apnoea is associated with adverse health consequences, including cardiometabolic comorbidities, the association with mild disease is unclear, and reports differ regarding the clinical relevance of mild obstructive sleep apnoea. Improved diagnostic techniques and evidence-based approaches to management in mild obstructive sleep apnoea require further research.

Utilizing inspiratory airflows during standard polysomnography to assess pharyngeal function in children during sleep

OBJECTIVES

Obstructive sleep apnea (OSA) is the result of pharyngeal obstruction that occurs predominantly during REM in children. Pathophysiologic mechanisms responsible for upper airway obstruction, however, are poorly understood. Thus, we sought to characterize upper airway obstruction in apneic compared to snoring children during sleep. We hypothesized that apneic compared to snoring children would exhibit an increased prevalence and severity of upper airway obstruction, that would be greater in REM compared to non-REM, and would improve following adenotonsillectomy.

STUDY DESIGN

Apneic children were assessed with routine polysomnography before and after adenotonsillectomy, and compared to snoring children matched for gender, age, and BMI z-score. In addition to traditional scoring metrics, the following were used to characterize upper airway obstruction: maximal inspiratory airflow (%VI max) and percent of time with inspiratory flow-limited breathing (%IFL).

RESULTS

OSA compared to snoring children had similar degrees of upper airway obstruction in non-REM; however, during REM, children with sleep apnea exhibited a higher %IFL (98 ± 2% vs.73 ± 8%, P < 0.01) and lower %VI max (56 ± 6 vs.93 ± 10%, P < 0.01). In children with OSA, CO2 levels were elevated during both wake and sleep. Following adenotonsillectomy, upper airway obstruction improved during REM manifest by decreased %IFL (98 ± 2 to 63 ± 9%, P = 0.04), increased %VI max (56 ± 6 to 95 ± 5%, P = 0.01) and decreased CO2 levels.

CONCLUSIONS

Differences in the prevalence and severity upper airway obstruction suggest impaired compensatory responses during REM in children with OSA, which improved following adenotonsillectomy.

Gastroesophageal Reflux Affects Sleep Quality in Snoring Obese Children

PURPOSE

This study was performed to evaluate the quality of sleep in snoring obese children without obstructive sleep apnea (OSA); and to study the possible relationship between sleep interruption and gastroesophageal reflux (GER) in snoring obese children.

METHODS

Study subjects included 13 snoring obese children who were referred to our sleep lab for possible sleep-disordered breathing. Patients underwent multichannel intraluminal impedance and esophageal pH monitoring with simultaneous polysomnography. Exclusion criteria included history of fundoplication, cystic fibrosis, and infants under the age of 2 years. Significant association between arousals and awakenings with previous reflux were defined by symptom-association probability using 2-minute intervals.

RESULTS

Sleep efficiency ranged from 67-97% (median 81%). A total of 111 reflux episodes (90% acidic) were detected during sleep, but there were more episodes per hour during awake periods after sleep onset than during sleep (median 2.3 vs. 0.6, p=0.04). There were 279 total awakenings during the sleep study; 56 (20.1%) of them in 9 patients (69.2%) were preceded by reflux episodes (55 acid, 1 non-acid). In 5 patients (38.5%), awakenings were significantly associated with reflux.

CONCLUSION

The data suggest that acid GER causes sleep interruptions in obese children who have symptoms of snoring or restless sleep and without evidence of OSA.

A gender-aware framework for the daytime detection of obstructive sleep apnea

Sleep is an activity that is necessary for our survival. While the body may be still during sleep, the brain is actively progressing through repeating cycles of light and deep sleep whose purpose is physical and mental recovery and regeneration. Obstructive sleep apnea (OSA) is a sleep disorder in which breathing is frequently and repeatedly stopped during sleep. OSA severely interrupts the normal sleep cycle and the regeneration work associated with it and can thus result in detrimental health consequences. OSA, with all the adverse health effects associated with it, places a significant burden on the US healthcare system. Polysomnography (PSG) - the gold standard OSA diagnostic test - is an overnight sleep test that monitors the biophysiological changes that occur during sleep. The test is notorious for its intrusiveness, discomfort, prohibitive cost, and scarcity - all reasons contributing to OSA being a severely underdiagnosed sleep disorder. In this paper, we propose a system that can serve as an early-stage OSA diagnostic tool that can non-intrusively, affordably and accurately screen patients for the disorder before proceeding with a full-night PSG. Unlike existing tools, our solution is gender-aware and does not rely on detecting apneic events in the data to make a diagnosis; rather, it is designed to trigger brain responses that are indicative of the disorder. Our tool can therefore make diagnoses even while patients are awake and breathing normally. The system was tested in a pilot study of 21 patients and our preliminary results show an average accuracy of 96.25%.

Nasal pressure recordings for automatic snoring detection

This study presents a rule-based method for automated, real-time snoring detection using nasal pressure recordings during overnight sleep. Although nasal pressure recordings provide information regarding nocturnal breathing abnormalities in a polysomnography (PSG) study or continuous positive airway pressure (CPAP) system, an objective assessment of snoring detection using these nasal pressure recordings has not yet been reported in the literature. Nasal pressure recordings were obtained from 55 patients with obstructive sleep apnea. The PSG data were also recorded simultaneously to evaluate the proposed method. This rule-based method for automatic, real-time snoring detection employed preprocessing, short-time energy and the central difference method. Using this methodology, a sensitivity of 85.4% and a positive predictive value of 92.0% were achieved in all patients. Therefore, we concluded that the proposed method is a simple, portable and cost-effective tool for real-time snoring detection in PSG and CPAP systems that does not require acoustic analysis using a microphone.

Respiratory modulations in the photoplethysmogram (DPOP) as a measure of respiratory effort

DPOP is a measure of the strength of respiratory modulations present in the pulse oximetry photoplethysmogram (pleth) waveform. It has been proposed as a non-invasive parameter for the prediction of the response to volume expansion in hypovolemic patients. The effect of resistive breathing on the DPOP parameter was studied to determine whether it may have an adjunct use as a measure of respiratory effort. Healthy volunteers were tasked to breathe at fixed respiratory rates over a range of airway resistances generated by a flow resistor inserted within a mouthpiece. Changes in respiratory efforts, effected by the subjects and measured as airway pressures at the mouth, were compared to DPOP values derived from a finger pulse oximeter probe. It was found that the increased effort to breathe manifests itself as an associated increase in DPOP. Further, a relationship between DPOP and percent modulation of the pleth waveform was observed. A version of the DPOP algorithm that corrects for low perfusion was implemented which resulted in an improved relationship between DPOP and PPV. Although a limited cohort of seven volunteers was used, the results suggest that DPOP may be useful as a respiratory effort parameter, given that the fluid level of the patient is maintained at a constant level over the period of analysis.

The role of flow limitation as an important diagnostic tool and clinical finding in mild sleep-disordered breathing

Obstructive sleep apnea (OSA) is defined by quantifying apneas and hypopneas along with symptoms suggesting sleep disruption. Subtler forms of sleep-disordered breathing can be missed when this criteria is used. Newer technologies allow for non-invasive detection of flow limitation, however consensus classification is needed. Subjects with flow limitation demonstrate electroencephalogram changes and clinical symptoms indicating sleep fragmentation. Flow limitation may be increased in special populations and treatment with nasal continuous positive airway pressure (CPAP) has been shown to improve outcomes. Titrating CPAP to eliminate flow limitation may be associated with improved clinical outcomes compared to treating apneas and hypopneas.

Night-to-Night Variability in Sleep Disordered Breathing and the Utility of Esophageal Pressure Monitoring in Suspected Obstructive Sleep Apnea

STUDY OBJECTIVE

Esophageal manometry (Pes) is the gold standard to detect repetitive episodes of increased respiratory effort followed by arousal (RERAs). Because RERAs are not included in the apnea-hypopnea index (AHI), we often refer patients with symptoms of sleep disordered breathing (SDB) and AHI < 5 for a second polysomnogram (PSG) with Pes. Often, the second PSG will demonstrate AHI > 5, confirming a diagnosis of OSA. We speculate that in most cases of suspected SDB, Pes does not add further diagnostic data and that night-to-night variability in OSA severity results in a first false-negative study.

METHODS

We conducted a retrospective review of PSGs between 2008 and 2012 in adults with initial PSG negative for OSA followed by a second study (with or without Pes) within 6 mo.

RESULTS

Of 125 studies that met inclusion criteria, a second study was completed with Pes in 105 subjects. SDB was diagnosed in 73 subjects (68.5%) completing a second PSG with Pes: 49 (46.7%) received a diagnosis based on AHI, and 24 (22.8%) received a diagnosis based on Pes (p = 0.003). There were no statistically significant differences in the mean AHI change between the two PSGs in subjects who completed the second study with or without Pes.

CONCLUSIONS

In patients with symptoms of SDB and initial PSG with AHI < 5, the majority met criteria for OSA on second PSG by AHI without additional information added by Pes. Because Pes is not widely available and is somewhat invasive, a repeat study without Pes may be sufficient to diagnose SDB.

Chronic fatigue syndrome and fibromyalgia in diagnosed sleep disorders: a further test of the 'unitary' hypothesis

BACKGROUND

Since chronic fatigue syndrome (CFS) and fibromyalgia (FM) often co-exist, some believe they reflect the same process, somatization. Against that hypothesis are data suggesting FM but not CFS was common in patients with sleep-disordered breathing (SDB). The presence of discrete case definitions for CFS and FM allowed us to explore rates of CFS alone, CFS with FM, and FM alone in SDB patients compared to those with sleep complaints that fulfilled criteria for insomnia.

METHODS

Participants were 175 sequential patients with sleep-related symptoms (122 had SDB and 21 had insomnia) and 39 healthy controls. Diagnoses were made by questionnaires, tender point count, and rule out labs; sleepiness was assessed with Epworth Sleepiness Scale and mood with Beck Depression Inventory.

RESULTS

Rates of CFS, FM or CFS + FM were high: 13% in SDB and 48% in insomnia. CFS occurred frequently in SDB and insomnia, but FM occurred frequently only in insomnia. SDB patients with CFS and/or FM had higher daytime sleepiness than those without these disorders.

CONCLUSION

CFS patients should complete Epworth scales, and sleep evaluation should be considered for those with scores ≥ 16 before receiving the diagnosis of CFS; the coexistence of depressed mood in these patients suggests some may be helped by treatment of their depression. That FM was underrepresented in SDB suggests FM and CFS may have different underlying pathophysiological causes.

New insights on the pathophysiology of inspiratory flow limitation during sleep

INTRODUCTION

Inspiratory flow limitation (IFL) is defined as a "flattened shape" of inspiratory airflow contour detected by nasal cannula pressure during sleep and can indicate increased upper airway resistance especially in mild sleep-related breathing disorders (SRBD). The objective of this study was to investigate the association between upper airway abnormalities and IFL in patients with mild SRBD.

METHODS

This study was derived from a general population study consisting of selected individuals with apnea-hypopnea index (AHI) below 5 events/h of sleep, ("no obstructive sleep apnea" group) and individuals with AHI between 5 and 15 events/h ("mild obstructive sleep apnea" group). A total of 754 individuals were divided into four groups: group 1: AHI <5/h and <30 % of total sleep time (TST) with IFL (515 individuals), group 2: AHI <5/h and >30 % of TST with IFL (46 individuals), group 3: AHI: 5-15/h and <30 % of TST with IFL (168 individuals), and group 4: AHI: 5-15/h and >30 % of TST with IFL (25 individuals).

RESULTS

Individuals with complains of oral breathing demonstrated a risk 2.7-fold larger of being group 4 compared with group 3. Abnormal nasal structure increased the chances of being in group 4 3.2-fold in comparison to group 1. Individuals with voluminous lateral wall demonstrated a risk 4.2-fold larger of being group 4 compared with group 3.

CONCLUSION

More than 30 % of TST with IFL detected in sleep studies was associated with nasal and palatal anatomical abnormalities in mild SRBD patients.

Response characteristics for thermal and pressure devices commonly used for monitoring nasal and oral airflow during sleep studies

We examined thermocouple and pressure cannulae responses to oral and nasal airflow using a polyester model of a human face, with patent nasal and oral orifices instrumented with a dual thermocouple (F-ONT2A, Grass) or a dual cannula (0588, Braebon) pressure transducer (± 10 cm H2O, Celesco) system. Tidal airflow was generated using a dual compartment facemask with pneumotachographs (Fleisch 2) connected to the model orifices. During nasal breathing: thermocouple amplitude = 0.38 Ln [pneumotachograph amplitude] + 1.31 and pressure cannula amplitude = 0.93 [pneumotachograph amplitude](2.15); during oral breathing: thermocouple amplitude = 0.44 Ln [pneumotachograph amplitude] + 1.07 and pressure cannula amplitude = 0.33 [pneumotachograph amplitude](1.72); (all range ∼ 0.1-∼ 4.0 L s(-1); r(2) > 0.7). For pneumotachograph amplitudes <1 L s(-1) (linear model) change in thermocouple amplitude/unit change in pneumotachograph amplitude was similar for nasal and oral airflow, whereas nasal pressure cannula amplitude/unit change in pneumotachograph amplitude was almost four times that for oral. Increasing oral orifice area from 0.33 cm(2) to 2.15 cm(2) increased oral thermocouple amplitude/unit change in pneumotachograph amplitude by ∼ 58% but decreased pressure cannula amplitude/unit change in pneumotachograph amplitude by 49%. For pneumotachograph amplitudes up to 1 L s(-1), alterations in inspiratory/expiratory ratios or total respiratory time did not affect the sensitivity of either nasal or oral pressure cannulae or the nasal thermocouple, but the oral thermocouple sensitivity was influenced by respiratory cycle time. Different nasal and oral responses influence the ability of these systems to quantitatively assess nasal and oral airflow and oro-nasal airflow partitioning.

Envelope analysis of the airflow signal to improve polysomnographic assessment of sleep disordered breathing

STUDY OBJECTIVES

Given the detailed respiratory waveform signal provided by the nasal cannula in polysomnographic (PSG) studies, to quantify sleep breathing disturbances by extracting a continuous variable based on the coefficient of variation of the envelope of that signal.

DESIGN

Application of an algorithm for envelope analysis to standard nasal cannula signal from actual polysomnographic studies.

SETTING

PSG recordings from a sleep disorders center were analyzed by an algorithm developed on the Igor scientific data analysis software.

PATIENTS OR PARTICIPANTS

Recordings representative of different degrees of sleep disordered breathing (SDB) severity or illustrative of the covariation between breathing and particularly relevant factors and variables.

INTERVENTIONS

The method calculated the coefficient of variation of the envelope for each 30-second epoch. The normalized version of that coefficient was defined as the respiratory disturbance variable (RDV). The method outcome was the all-night set of RDV values represented as a time series.

MEASUREMENTS AND RESULTS

RDV quantitatively reflected departure from normal sinusoidal breathing at each epoch, providing an intensity scale for disordered breathing. RDV dynamics configured itself in recognizable patterns for the airflow limitation (e.g., in UARS) and the apnea/hypopnea regimes. RDV reliably highlighted clinically meaningful associations with staging, body position, oximetry, or CPAP titration.

CONCLUSIONS

Respiratory disturbance variable can assess sleep breathing disturbances as a gradual phenomenon while providing a comprehensible and detailed representation of its dynamics. It may thus improve clinical diagnosis and provide a revealing descriptive tool for mechanistic sleep disordered breathing modeling. Respiratory disturbance variable may contribute to attaining simplified screening methodologies, novel diagnostic criteria, and insightful research tools.