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Background and Purpose: Overnight shifts of fluid from lower to upper compartments exacerbate obstructive sleep apnea (OSA) in some OSA populations. Given the high prevalence of OSA after stroke, decreased mobility and use of IV fluids among hospitalized patients with stroke, and improvement in OSA in the months after stroke, we hypothesized that overnight fluid shifts occur and are associated with OSA among patients with subacute ischemic stroke. Methods: Within a population-based project, we performed overnight sleep apnea tests (ApneaLink Plus) during ischemic stroke hospitalizations. Before sleep that evening, and the following morning before rising from bed, we assessed neck and calf circumference, and leg fluid volume (bioimpedance spectroscopy). The average per subject overnight change in the 3 fluid shift measurements was calculated and compared with zero. Linear regression was used to test the crude association between each of the 3 fluid shift measurements and the respiratory event index (REI). Results: Among the 292 participants, mean REI was 24 (SD=18). Within individuals, calf circumference decreased on average by 0.66 cm (SD=0.75 cm, P P P =0.07). In men, when the overnight change of calf circumference was negative, an interquartile range (0.8 cm) decrease in calf circumference overnight was significantly associated with a 25.1% increase in REI ( P =0.02); the association was not significant in women. The relationship between overnight change in leg fluid volume and REI was U shaped. Conclusions: This population-based, multicenter, cross-sectional study showed that in hospitalized patients with ischemic stroke, nocturnal rostral fluid shifts occurred, and 2 of the 3 measures were associated with greater OSA severity. Interventions that limit overnight fluid shifts should be tested as potential treatments for OSA among patients with subacute ischemic stroke.

One of the most important signals to assess respiratory function, especially in patients with sleep apnea, is airflow. A convenient method to estimate airflow is based on analyzing tracheal sounds and movements. However, this method requires accurate identification of respiratory phases. Our goal is to develop an automatic algorithm to analyze tracheal sounds and movements to identify respiratory phases during sleep. Data from adults with suspected sleep apnea who were referred for in-laboratory sleep studies were included. Simultaneously with polysomnography, tracheal sounds and movements were recorded with a small wearable device attached to the suprasternal notch. First, an adaptive detection algorithm was developed to localize the respiratory phases in tracheal sounds. Then, for each phase, a set of morphological features from sound energy and tracheal movement were extracted to classify the localized phases into inspirations or expirations. The average error and time delay of detecting respiratory phases were 7.62% and 181 ms during normal breathing, 8.95% and 194 ms during snoring, and 13.19% and 220 ms during respiratory events, respectively. The average classification accuracy was 83.7% for inspirations and 75.0% for expirations. Respiratory phases were accurately identified from tracheal sounds and movements during sleep.

Purpose The current gold standard to detect sleep/wakefulness is based on electroencephalogram, which is inconvenient if included in portable sleep screening devices. Therefore, a challenge in the portable devices is sleeping time estimation. Without sleeping time, sleep parameters such as apnea/hypopnea index (AHI), an index for quantifying sleep apnea severity, can be underestimated. Recent studies have used tracheal sounds and movements for sleep screening and calculating AHI without considering sleeping time. In this study, we investigated the detection of sleep/wakefulness states and estimation of sleep parameters using tracheal sounds and movements. Materials and Methods Participants with suspected sleep apnea who were referred for sleep screening were included in this study. Simultaneously with polysomnography, tracheal sounds and movements were recorded with a small wearable device, called the Patch, attached over the trachea. Each 30-second epoch of tracheal data was scored as sleep or wakefulness using an automatic classification algorithm. The performance of the algorithm was compared to the sleep/wakefulness scored blindly based on the polysomnography. Results Eighty-eight subjects were included in this study. The accuracy of sleep/wakefulness detection was 82.3±8.66% with a sensitivity of 87.8±10.8 % (sleep), specificity of 71.4±18.5% (awake), F1 of 88.1±9.3% and Cohen's kappa of 0.54. The correlations between the estimated and polysomnography-based measures for total sleep time and sleep efficiency were 0.78 (p

Previous studies based on magnetic resonance imaging (MRI) or computed tomography (CT) have shown that pharyngeal airway diameter during wakefulness is different between healthy controls and patients with a high risk of sleep-disordered breathing (SDB). However, MRI and CT are expensive and not easily accessible. Conversely, ultrasonography is more accessible and is getting more attention as a point-of-care technology to assess physiologic systems, such as the pharynx. Thus, we aimed to evaluate the feasibility of ultrasonography in estimating the pharyngeal airway dimension. To evaluate the pharyngeal airway with ultrasonography, we measured the parasagittal anterior-posterior (PAP) diameter and transverse diameter. For PAP diameter measurements, the transducer probe was placed in a submandibular lateral oblique position, with its superior margin abutting the angle of the left mandible. For the transverse measurement, the ultrasound probe was positioned in a submandibular location, in a near-coronal plane, just above the hyoid bone so that the tongue could be seen in cross-section. The diameter measurements were performed manually by two technicians. The reliability of these measurements was assessed by the intra-class correlation coefficient (ICC). To validate our measurements, we compared the measured PAP diameter with the average pharyngeal airway cross-sectional area from vellum to glottis measured by acoustic pharyngometry. Furthermore, we compared the influence of obesity and SDB in the measured pharyngeal diameters. Eighteen controls and 13 individuals with a high risk of SDB participated in this study. Reliability analysis of the PAP measurements yielded an ICC of 0.97 (95% confidence interval: 0.94-0.98). Furthermore, measured PAP diameters were significantly correlated with the pharyngeal airway cross-sectional area (r = 0.76, p0.01). Moreover, obesity and SDB were associated with decreases in PAP diameter. Our study shows that ultrasonography measurement of the PAP diameter may provide a quantitative assessment of the pharyngeal airway and may be useful for screening of SDB.

Study objective To develop an algorithm for improving apnea hypopnea index (AHI) estimation which includes event by event validation and event duration estimation. The algorithm uses breathing sounds, respiratory related movements and blood oxygen saturation (SaO2). Methods Adults with suspected sleep apnea underwent overnight polysomnography (PSG) at Toronto Rehabilitations Institute. Simultaneously with PSG, breathing sounds and respiratory related movements were recorded over the suprasternal notch using the Patch. The Patch had a microphone and an accelerometer to record respiratory sounds and movement, respectively. First, we calculated the amount of drops in SaO2 from pulse oximeter. Subsequently, energy of breaths and accelerometer were extracted. Features were normalized, weighted, summed and passed through a threshold to estimate PatchAHI. PatchAHI was compared to the AHI obtained from PSG (PSGAHI). Furthermore, performance of event detection was evaluated using F1-score. Moreover, event duration difference between estimated and PSG-based events was compared. Results Data from 69 subjects were investigated. PatchAHI had high correlation with PSGAHI (r2 = 0.88). Considering a diagnostic AHI cut-off of ≥15, sensitivity and specificity were 91.42 ± 11.92% and 89.29 ± 7.62%, respectively. F1-score for individual event detection increased from 0.22 ± 0.10 for AHI≤5 to 0.72 ± 0.09 for AHI >30. Moreover, event duration difference between estimated events and PSG-based events was 5.33 ± 8.17 sec. Conclusion Our proposed algorithm had high accuracy in estimating individual respiratory events during sleep. The algorithm can increase reliability of acoustic methods for diagnosis of sleep apnea at home.

Objective: Sleep apnea is a chronic respiratory disorder and its standard assessment requires full night in-laboratory polysomnography (PSG). However, PSG is expensive, time-consuming, and inconvenient. Thus, there is a need to monitor sleep apnea with more convenient wearable devices. The objective of this study was to implement deep learning algorithms to monitor sleep apnea severity based on respiratory movements that can be easily recorded over the trachea. Methods: Adult individuals referred to the sleep laboratory at the Toronto Rehabilitation Institute for overnight sleep studies were included (N=69). Simultaneously with the PSG, an accelerometer was attached to the participant's suprasternal notch to record tracheal respiratory movements. Twenty-one features were extracted from the tracheal movements and used in a deep learning classifier to detect respiratory events. The apnea hypopnea index (AHI) was estimated as the number of events per hour of sleep. Results: The F1 score of the event-by-event detection algorithm was between 12% and 71% for different groups of sleep apnea severity. There was a strong correlation between the estimated and the PSG-derived AHI (r=0.86, p

Sleep apnea can be characterized by reductions in the respiratory tidal volume. Previous studies showed that the tidal volume can be estimated from tracheal sounds and movements called tracheal signals. Additionally, tracheal sounds include the sounds of snoring, a common symptom of obstructive sleep apnea. This study investigates the feasibility of estimating the severity of sleep apnea, as quantified by the apnea/hypopnea index (AHI), using the estimated tidal volume and snoring sounds extracted from tracheal signals. Tracheal signals were recorded simultaneously with polysomnography (PSG). The tidal volume was estimated from tracheal signals. The reductions in the tidal volume were detected as potential respiratory events. Additionally, features related to snoring sounds, which quantified variability, temporal clusters, and dominant frequency of snores, were extracted. A step-wise regression model and a greedy search algorithm were used sequentially to select the optimal set of features to estimate the apnea/hypopnea index and classify participants into healthy individuals and patients with sleep apnea. Sixty-one participants with suspected sleep apnea (age: 51 ± 16, body mass index: 29.5 ± 6.4 kg/m2 , apnea/hypopnea index: 20.2 ± 21.2 event/h) who were referred for a sleep test were recruited. The estimated apnea/hypopnea index was strongly correlated with the polysomnography-based apnea/hypopnea index (R2 = 0.76, p

Fluid that shifts out of the legs and into the neck when supine can contribute to upper-airway narrowing. The present study investigates the relative contributions of vascular and extravascular fluid to the total accumulation of neck fluid volume (NFV). In 22 healthy awake participants (8 women), aged 42 ± 9 yr, we measured NFV with bioelectrical impedance, internal jugular vein volume (IJVV) with ultrasound, and extravascular NFV (NFVEV) as the difference between NFV and IJVV. Participants were randomly allocated to control and intervention, both of which were conducted on the same day. Measurements were made at baseline and every 5 min thereafter during control and intervention. During intervention, participants received 40 mmHg lower-body positive pressure (LBPP) when supine, followed by LBPP plus 10° Trendelenburg position, then LBPP when supine again, followed by recovery. During control, participants lay supine for equal time. LBPP and LBPP plus Trendelenburg position both increased NFV from baseline compared with control ( P < 0.001), with significant contributions from IJVV ( P < 0.001). Returning to supine with LBPP, IJVV returned to baseline, but NFV remained elevated because of accumulation of NFVEV. These findings suggest that contributions of IJVV to NFV are immediate but transient, whereas sustained elevation in NFV when supine is likely a result of NFVEV. These findings add new insights into the mechanism by which fluid accumulates in the neck by rostral fluid shift. NEW & NOTEWORTHY This study demonstrates that lying supine for 30 min as well as increased fluid shift out of the legs to simulate nocturnal rostral fluid shift causes fluid to accumulate mainly in the extravascular space of the neck rather than in the internal jugular veins. Therefore, in subjects without fluid-retaining states, extravascular neck fluid accumulation overnight might play a more significant role in the pathophysiology of upper-airway narrowing than intravascular fluid accumulation.

Study objectives Overnight fluid shift from the legs into the neck may contribute to the pathogenesis of snoring and obstructive sleep apnea (OSA). The present study investigates the effects of calf muscle electrical stimulation (ES) on reducing leg fluid accumulation while seated, subsequent rostral fluid shift on lying down, and the impact on snoring and OSA. Methods Sixteen non-obese, normotensive men with OSA participated in the study. On the first study day, participants sat for 150 min receiving either active or sham ES through random allocation, then lied supine for 60 min. While seated and supine, leg and neck fluid volumes were measured using bioelectrical impedance to determine the magnitude of fluid shift. On the night of the study day, participants wore a portable sleep apnea diagnostic device overnight to measure snoring and sleep apnea severity. One week later, participants crossed over to the other study condition. Results Active calf muscle ES reduced leg fluid accumulation by 46% while seated. Upon lying supine, active ES reduced fluid shift out of the legs by 17% and reduced neck fluid accumulation by 31%. This led to a 15% reduction in snoring index, but did not alleviate OSA. Conclusions One session of calf muscle ES was effective at reducing leg fluid accumulation and rostral fluid shift, which led to a modest reduction in the snoring index, but not OSA. Despite this lack of effect of calf muscle ES in attenuating OSA severity, the reduction in the snoring index suggests that it did have an effect, albeit mild, on upper-airway mechanics.

A reliable, accessible, and non-intrusive method for tracking respiratory and heart rate is important for improving monitoring and detection of sleep apnea. In this study, an algorithm based on motion analysis of infrared video recordings was validated in 50 adults referred for clinical overnight polysomnography (PSG). The algorithm tracks the displacements of selected feature points on each sleeping participant and extracts respiratory rate using principal component analysis and heart rate using independent component analysis. For respiratory rate estimation (mean ± standard deviation), 89.89 % ± 10.95 % of the overnight estimation was accurate within 1 breath per minute compared to the PSG-derived respiratory rate from the respiratory inductive plethysmography signal, with an average root mean square error (RMSE) of 2.10 ± 1.64 breaths per minute. For heart rate estimation, 77.97 % ± 18.91 % of the overnight estimation was within 5 beats per minute of the heart rate derived from the pulse oximetry signal from PSG, with mean RMSE of 7.47 ± 4.79 beats per minute. No significant difference in estimation of RMSE of either signal was found according to differences in body position, sleep stage, or amount of the body covered by blankets. This vision-based method may prove suitable for overnight, non-contact monitoring of respiratory rate. However, at present, heart rate monitoring is less reliable and will require further work to improve accuracy., Our proposed algorithm tracks the movement of selected feature points from a video of a sleeping participant, and extracts respiratory rate using principal component analysis and heart rate using independent component analysis.

Obstructive sleep apnea (OSA) is a respiratory disorder characterized by interruption to breathing during sleep. Usually, the OSA is more severe in the supine sleeping position. Recent studies also demonstrated that the head position may play an important role in the pathophysiology of the OSA. Therefore, monitoring the sleeping body and the head position has high clinical importance to optimize the treatment of the OSA. In this paper, three machine learning approaches were used to detect the head position during sleep in infrared images. In the first two methods, supervised classifiers were trained to estimate the head position based on different feature sets extracted from infrared images. In the third method, three different convolutional neural network (CNN) structures (ResNet50, MobileNet, and Darknet19) were trained to detect the head position during sleep. To detect the body position, the same CNN architectures were trained on infrared images. Overnight sleeping data (sleep duration =5±1h) from 50 participants (age: 53 ± 15 years, BMI: 29 ± 6 kg/m2, and 30 men/20 women) with various levels of OSA severity as measured by the apnea-hypopnea index (AHI =25±29 events/h and OSA severity: 12 normal, 13 mild, 11 moderate, and 14 severe) were collected for this paper. The models were trained on the data collected in one laboratory room from half of the participants and tested on the data from the other half collected in a different laboratory room. The best performing model (Darknet19) correctly estimated the lateral versus supine head position with 92% accuracy and 94% F1-Score and the lateral versus supine body position with 87% accuracy and 87% F1-Score.

Background Sleep apnea is a respiratory disorder characterized by frequent breathing cessation during sleep. Sleep apnea severity is determined by the apnea-hypopnea index (AHI), which is the hourly rate of respiratory events. In positional sleep apnea, the AHI is higher in the supine sleeping position than it is in other sleeping positions. Positional therapy is a behavioral strategy (eg, wearing an item to encourage sleeping toward the lateral position) to treat positional apnea. The gold standard of diagnosing sleep apnea and whether or not it is positional is polysomnography; however, this test is inconvenient, expensive, and has a long waiting list. Objective The objective of this study was to develop and evaluate a noncontact method to estimate sleep apnea severity and to distinguish positional versus nonpositional sleep apnea. Methods A noncontact deep-learning algorithm was developed to analyze infrared video of sleep for estimating AHI and to distinguish patients with positional vs nonpositional sleep apnea. Specifically, a 3D convolutional neural network (CNN) architecture was used to process movements extracted by optical flow to detect respiratory events. Positional sleep apnea patients were subsequently identified by combining the AHI information provided by the 3D-CNN model with the sleeping position (supine vs lateral) detected via a previously developed CNN model. Results The algorithm was validated on data of 41 participants, including 26 men and 15 women with a mean age of 53 (SD 13) years, BMI of 30 (SD 7), AHI of 27 (SD 31) events/hour, and sleep duration of 5 (SD 1) hours; 20 participants had positional sleep apnea, 15 participants had nonpositional sleep apnea, and the positional status could not be discriminated for the remaining 6 participants. AHI values estimated by the 3D-CNN model correlated strongly and significantly with the gold standard (Spearman correlation coefficient 0.79, P Conclusions This study demonstrates the possibility of using a camera-based method for developing an accessible and easy-to-use device for screening sleep apnea at home, which can be provided in the form of a tablet or smartphone app.

Airflow is the reference signal to assess sleep respiratory disorders, such as sleep apnea. Previous studies estimated airflow using tracheal sounds in short segments with specific airflow rates, while requiring calibration or a few breaths for tuning the relationship between sound energy and airflow. Airflow-sound relationship can change by posture, sleep stage and airflow rate or tidal volume. We investigated the possibility of estimating surrogates of tidal volume without calibration in the adult sleep apnea population using tracheal sounds and movements. Two surrogates of tidal volume: thoracoabdominal range of sum movement and airflow level were estimated. Linear regression was used to estimate thoracoabdominal range of sum movement from sound energy and the range of movements. The sound energy lower envelope was found to correlate with airflow level. The agreement between reference and estimated signals was assessed by repeated-measure correlation analysis. The estimated tidal volumes were used to estimate the airflow signal. Sixty-one participants (30 females, age: 51 ± 16 years, body mass index: 29.5 ± 6.4 kg m-2 , and apnoea-hypopnea index: 20.2 ± 21.2) were included. Reference and estimated thoracoabdominal range of sum movement of whole night data were significantly correlated with the reference signal extracted from polysomnography (r = 0.5 ± 0.06). Similarly, significant correlations (r = 0.3 ± 0.05) were found for airflow level. Significant differences in estimated surrogates of tidal volume were found between normal breathing and apnea/hypopnea. Surrogate of airflow can be extracted from tracheal sounds and movements, which can be used for assessing the severity of sleep apnea and even phenotyping sleep apnea patients based on the estimated airflow shape.

To reduce the spread of the severe acute respiratory syndrome coronavirus 2, many pulmonary function testing (PFT) laboratories have been closed or have significantly reduced their testing capacity. Because these mitigation strategies may be necessary for the next 6 to 18 months to prevent recurrent peaks in disease prevalence, fewer objective measurements of lung function will alter the diagnosis and care of patients with chronic respiratory diseases. PFT, which includes spirometry, lung volume, and diffusion capacity measurement, is essential to the diagnosis and management of patients with asthma, COPD, and other chronic lung conditions. Both traditional and innovative alternatives to conventional testing must now be explored. These may include peak expiratory flow devices, electronic portable spirometers, portable exhaled nitric oxide measurement, airwave oscillometry devices, and novel digital health tools such as smartphone microphone spirometers and mobile health technologies along with integration of machine learning approaches. The adoption of some novel approaches may not merely replace but could improve existing management strategies and alter common diagnostic paradigms. With these options comes important technical, privacy, ethical, financial, and medicolegal barriers that must be addressed. However, the coronavirus disease 19 pandemic also presents a unique opportunity to augment conventional testing by including innovative and emerging approaches to measuring lung function remotely in patients with respiratory disease. The benefits of such an approach have the potential to enhance respiratory care and empower patient self-management well beyond the current global pandemic.

Tracheal sounds represent information about the upper airway and respiratory airflow, however, they can be contaminated by the snoring sounds. The sound of snoring has spectral content in a wide range that overlaps with that of breathing sounds during sleep. For assessing respiratory airflow using tracheal breathing sound, it is essential to remove the effect of snoring. In this paper, an automatic and unsupervised wavelet-based snoring removal algorithm is presented. Simultaneously with full-night polysomnography, the tracheal sound signals of 9 subjects with different levels of airway obstruction were recorded by a microphone placed over the trachea during sleep. The segments of tracheal sounds that were contaminated by snoring were manually identified through listening to the recordings. The selected segments were automatically categorized based on including discrete or continuous snoring pattern. Segments with discrete snoring were analyzed by an iterative wave-based filtering optimized to separate large spectral components related to snoring from smaller ones corresponded to breathing. Those with continuous snoring were first segmented into shorter segments. Then, each short segments were similarly analyzed along with a segment of normal breathing extracted from the recordings during wakefulness. The algorithm was evaluated by visual inspection of the denoised sound energy and comparison of the spectral densities before and after removing snores, where the overall rate of detectability of snoring was less than 2%.Clinical Relevance- The algorithm provides a way of separating snoring pattern from the tracheal breathing sounds. Therefore, each of them can be analyzed separately to assess respiratory airflow and the pathophysiology of the upper airway during sleep.

Background: The high prevalence of obstructive sleep apnea (OSA) in asthmatics and its relationship to asthma severity are recognized, suggesting a pathophysiological link between the two, but the underlining mechanism for this has not been identified. We aimed to examine the role of fluid shift as a potential link and hypothesized that exaggerated negative intrathoracic pressure during OSA in asthmatics will increase thoracic fluid volume (TFV) and increase lower airway resistance (RLA). Methods: We randomized asthmatics to breathe normally (control) or perform 25 Mueller maneuvers (MM) by inspiring against a closed mouthpiece for 30 mins while supine to simulate obstructive apneas (intervention). Participants then crossed over to the other arm. Before and after each arm, we measured TFV and indices of lower airway narrowing assessed by oscillometry, including respiratory system reactance at 5Hz (X5), difference between resistance at 5 and 19Hz (R5-19) and reactance area (Ax). Results: MM arm significantly increased ΔTFV (Fig 1A), reduced ΔX5 (Fig 1B), increased ΔR5-19 (Fig1C) and increased ΔAx (Fig 1D) indicating an increase in RLA. Conclusion: MMs draw fluid into the thorax in asthmatics, accompanied by increased RLA. This suggests that increases in TFV resulting from negative intrathoracic pressure swings during obstructive apneas are one mechanism by which OSA could worsen asthma at night.

Introduction: Previous studies based on MRI or CT have shown that pharyngeal airway (PA) diameter during wakefulness is different between healthy subjects and sleep apnea patients. However, MRI or CT is expensive and is not easily accessible. Conversely, ultrasonography (US) is more accessible and is getting more attention as a point of care technology to assess physiological systems, such as pharynx. Thus, we aim to investigate the feasibility of US to assess the PA size. Methods: Average PA cross-sectional area (PAXSA) from vellum to glottis was measured by acoustic pharyngometry. To evaluate the PA with US, the transducer probe was placed in a submandibular lateral oblique position, with its superior margin abutting the angle of the left mandible (Fig. 1a). The airway appeared as a curved inverted U-shaped echogenic line during normal respiration (Fig. 1b). We measured the distance between the curved lines and referred to it as the parasagittal anterior-posterior (PAP) diameter. These measurements were performed manually by two technicians. Results: Among 32 subjects, the PAP diameters were clearly visible in 31 subjects (Age: 43.22±14.89 years, BMI: 28.32±6.20 kg/m2). Reliability analysis of the PAP measurements had an ICC of 0.97 (confidence interval: 0.94-0.98). Furthermore, PAP diameters were significantly correlated with the PAXSA (Fig. 1c). Discussion: Our preliminary study showed that the US can be used to measure the PA size.

Assessment of the pharyngeal airway is becoming important for delivering personalized treatment and better management of sleep apnea. However, evaluation of the pharyngeal airway area is difficult in the current state of the art. It is essential to use simple and accessible technology to measure the pharyngeal airway area. As vowel sounds are generated by vocal cords vibration and characterized by the pharyngeal airway, vowel sounds have the potential to evaluate the pharyngeal airway area. The objective of this study was to investigate the relationship between acoustic features of vowel sounds and the pharyngeal airway cross-sectional area (PAXSA) between soft palate and glottis. Twenty subjects were included in this study whose PAXSA was measured by acoustic pharyngometry. Vowel sounds were recorded with a microphone while lying supine. Vowel sound average power was calculated in different frequency ranges of 100-3000 Hz, 100-500 Hz, 500-1000 Hz, 1000-1500 Hz, 1500-2000 Hz, 2000-2500 Hz and 2500-3000 Hz. Statistical analysis showed that the decreases in the PAXSA were strongly correlated with the higher average power of vowel sounds in all frequency ranges. These results showed that individuals with low PAXSA might articulate the vowel in higher intensity. Clinical Relevance - This study demonstrates that the pharyngeal airway cross-sectional area during normal breathing has a significant effect on vowel articulation. Thus, vowel sound features can be used to estimate the resting pharyngeal airway cross-sectional area.

Introduction Although the precise cause of obstructive sleep apnea (OSA) remains unknown, various anatomical or structural factors are thought to influence upper airway patency. Recent clinical studies show that OSA is frequently observed among patients with fluid‐retaining states, such as heart/renal failure and postsurgery. It is important to note that a cause–effect relationship is not yet established, and our understanding of the effects of fluid overload is limited. The goal of this study was to investigate an animal model that can characterize the physiological changes that occur in response to fluid overload. Method Acute nonsurvival experiments were conducted in 16 Sprague–Dawley rats. Rats were initially anesthetized by inhaled isoflurane, while the femoral vein was cannulated and urethane (1.2–1.5 g/Kg body weight) was gradually delivered intravenously to induce anesthesia. Additional doses of urethane were delivered as necessary to maintain a surgical plane of anesthesia. A surgical incision was made on the cervical area to catheterize carotid artery to measure blood pressure. A pair of stainless‐steel wires was injected into the tongue to measure genioglossus muscle activity (GGEMG). All physiological measurements were recorded as intravenous infusion of saline was provided to the rat (infusion rate = 22 ml/kg over 30 min). Results Acute saline overloading resulted in a 33% decrease in GGEMG, when compared to baseline. There was also a gradual drop in the respiratory rate (13% decrease) that reached statistical significance at 10 min after infusion was stopped. The blood pressure exhibited a 14% increase which subsequently returned to baseline within 40 min stopping infusion. There were no significant changes in the heart rate. Conclusion The results of this study indicate that systemic fluid overload can affect significant changes in different physiological systems including reduction in genioglossus muscle activity, increase in blood pressure, and change autonomic nervous system function., In this study, the effects of acute intravenous saline infusion on multiple physiological parameters—such as respiratory, cardiovascular, and autonomic nervous system—which was conducted in urethane anesthetized rodents were evaluated. Our results showed that “fluid overloading” causes a significant decrease in upper airway dilator muscle activity, which is a characteristic biomarker observed in obstructive sleep apnea patients.

Background Sleep apnea is a respiratory disorder characterized by an intermittent reduction (hypopnea) or cessation (apnea) of breathing during sleep. Depending on the presence of a breathing effort, sleep apnea is divided into obstructive sleep apnea (OSA) and central sleep apnea (CSA) based on the different pathologies involved. If the majority of apneas in a person are obstructive, they will be diagnosed as OSA or otherwise as CSA. In addition, as it is challenging and highly controversial to divide hypopneas into central or obstructive, the decision about sleep apnea type (OSA vs CSA) is made based on apneas only. Choosing the appropriate treatment relies on distinguishing between obstructive apnea (OA) and central apnea (CA). Objective The objective of this study was to develop a noncontact method to distinguish between OAs and CAs. Methods Five different computer vision-based algorithms were used to process infrared (IR) video data to track and analyze body movements to differentiate different types of apnea (OA vs CA). In the first two methods, supervised classifiers were trained to process optical flow information. In the remaining three methods, a convolutional neural network (CNN) was designed to extract distinctive features from optical flow and to distinguish OA from CA. Results Overnight sleeping data of 42 participants (mean age 53, SD 15 years; mean BMI 30, SD 7 kg/m2; 27 men and 15 women; mean number of OA 16, SD 30; mean number of CA 3, SD 7; mean apnea-hypopnea index 27, SD 31 events/hour; mean sleep duration 5 hours, SD 1 hour) were collected for this study. The test and train data were recorded in two separate laboratory rooms. The best-performing model (3D-CNN) obtained 95% accuracy and an F1 score of 89% in differentiating OA vs CA. Conclusions In this study, the first vision-based method was developed that differentiates apnea types (OA vs CA). The developed algorithm tracks and analyses chest and abdominal movements captured via an IR video camera. Unlike previously developed approaches, this method does not require any attachment to a user that could potentially alter the sleeping condition.

Individuals with obstructive sleep apnea (OSA) can experience partial or complete collapse of the upper airway during sleep. This condition affects between 10-17% of adult men and 3-9% of adult women, requiring arousal to resume regular breathing. Frequent arousals disrupt proper sleeping patterns and cause daytime sleepiness. Untreated OSA has been linked to serious medical issues including cardiovascular disease and diabetes. Unfortunately, diagnosis rates are low (∼20%) and current sleep monitoring options are expensive, time consuming, and uncomfortable. Toward the development of a convenient, noncontact OSA monitoring system, this paper presents a simple, computer vision-based method to monitor cardiopulmonary signals (respiratory and heart rates) during sleep. System testing was performed with 17 healthy participants in five different simulated sleep positions. To monitor cardiopulmonary rates, distinctive points are automatically detected and tracked in infrared image sequences. Blind source separation is applied to extract candidate signals of interest. The optimal respiratory and heart rates are determined using periodicity measures based on spectral analysis. Estimates were validated by comparison to polysomnography recordings. The system achieved a mean percentage error of 3.4% and 5.0% for respiratory rate and heart rate, respectively. This study represents an important step in building an accessible, unobtrusive solution for sleep apnea diagnosis.

Prolonged sitting may promote leg fluid retention that redistributes to the neck during sleep and contributes to snoring. This could be attenuated by calf activity while sitting. In 16 healthy non-obese subjects we measured leg fluid volume (LFV) below the knees using bioelectrical impedance while sitting for 4h, snoring using a portable BresoDx™ device, and Mallampati grade. Using a double cross-over study design, subjects were randomized to one of two arms and crossed-over one week later: control arm - no calf exercise while sitting; intervention arm - calf contraction against a pedal resistance while sitting. The effects of sitting±calf activity on LFV and snoring were compared. We found that LFV increased by 216±101.0ml (p

In asthma, supine posture and sleep increase intrathoracic airway narrowing. When humans are supine, because of gravity fluid moves out of the legs and accumulates in the thorax. We hypothesized that fluid shifting out of the legs into the thorax contributes to the intrathoracic airway narrowing in asthma. Healthy and asthmatic subjects sat for 30 min and then lay supine for 30 min. To simulate overnight fluid shift, supine subjects were randomized to receive increased fluid shift out of the legs with lower body positive pressure (LBPP, 10-30 min) or none (control) and crossed over. With forced oscillation at 5 Hz, respiratory resistance (R5) and reactance (X5, reflecting respiratory stiffness) and with bioelectrical impedance, leg and thoracic fluid volumes (LFV, TFV) were measured while subjects were seated and supine (0 min, 30 min). In 17 healthy subjects (age: 51.8 ± 10.9 yr, FEV1/FVC z score: −0.4 ± 1.1), changes in R5 and X5 were similar in both study arms ( P > 0.05). In 15 asthmatic subjects (58.5 ± 9.8 yr, −2.1 ± 1.3), R5 and X5 increased in both arms (ΔR5: 0.6 ± 0.9 vs. 1.4 ± 0.8 cmH2O·l−1·s−1, ΔX5: 0.3 ± 0.7 vs. 1.1 ± 0.9 cmH2O·l−1·s−1). The increases in R5 and X5 were 2.3 and 3.7 times larger with LBPP than control, however ( P = 0.008, P = 0.006). The main predictor of increases in R5 with LBPP was increases in TFV (r = 0.73, P = 0.002). In asthmatic subjects, the magnitude of increases in X5 with LBPP was comparable to that with posture change from sitting to supine (1.1 ± 0.9 vs. 1.4 ± 0.9 cmH2O·l−1·s−1, P = 0.32). We conclude that in asthmatic subjects fluid shifting from the legs to the thorax while supine contributed to increases in the respiratory resistance and stiffness. NEW & NOTEWORTHY In supine asthmatic subjects, application of positive pressure to the lower body caused appreciable increases in respiratory system resistance and stiffness. Moreover, these changes in respiratory mechanics correlated positively with increase in thoracic fluid volume. These findings suggest that fluid shifts from the lower body to the thorax may contribute to overnight intrathoracic airway narrowing and worsening of asthma symptoms.

Rationale: End-stage renal disease (ESRD) patients have high annual mortality mainly due to cardiovascular causes. The acute effects of obstructive and central sleep apnea on cardiac function in ESRD patients have not been determined. We therefore tested, in patients with ESRD, the hypotheses that (1) sleep apnea induces deterioration in cardiac function overnight and (2) attenuation of sleep apnea severity by ultrafiltration (UF) attenuates this deterioration. Methods: At baseline, ESRD patients, on conventional hemodialysis, with left ventricular ejection fraction (LVEF) >45% had polysomnography (PSG) performed on a non-dialysis day to determine the apnea-hypopnea index (AHI). Echocardiography was performed at the bedside, before and after sleep. Isovolumetric contraction time divided by left ventricular ejection time (IVCT/ET) and isovolumetric relaxation time divided by ET (IVRT/ET) were measured by tissue doppler imaging. The myocardial performance index (MPI), a composite of systolic and diastolic function was also calculated. One week later, subjects with sleep apnea (AHI ≥15) had fluid removed by UF, followed by repeat PSG and echocardiography. ­Results: Fifteen subjects had baseline measurements, of which 7 had an AHI p = 0.008, 0.007 and 0.031, respectively), indicating deterioration in systolic and diastolic function. Following fluid removal by UF in the sleep-apnea group, the AHI decreased by 48.7% (p = 0.012) and overnight increases in MPI, IVCT/ET and IVRT/ET observed at baseline were abolished. Conclusions: In ESRD, cardiac function deteriorates overnight in those with sleep apnea, but not in those without sleep apnea. This overnight deterioration in the sleep-apnea group may be at least partially due to sleep apnea, since attenuation of sleep apnea by UF was accompanied by elimination of this deleterious overnight effect.

Introduction: Sleep apnea (SA) is clinically assessed using respiratory signals: thoracoabdominal (TA) movements and airflow, recorded during overnight polysomnography (PSG). A simpler portable device with the ability of estimating respiratory signals is more convenient and cost-effective. Tracheal sound and movements can be conveniently recorded with simple instruments. Objectives: To estimate respiratory signals using tracheal sounds and movements. Methods: Participants >18 yrs with suspected sleep apnea underwent overnight PSG. Simultaneously, tracheal sound and movements were recorded using an embedded microphone and accelerator placed over the tracheal notch. Linear regression was used to estimate TA movements from sound energy and the range of movements. Also, the sound energy envelope was extracted as a surrogate of airflow level. Repeated measure correlation was used to calculate the agreement between reference and estimated signals in normal breathing, apneas and hypopneas. Results: Forty participants (21 females, age: 49 ± 16, BMI: 29.5 ± 6.1, and AHI: 17.9 ± 19.6) were included. Estimated movements correlated significantly with TA movements during normal breathing (0.53±0.06*), hypopneas (0.65±0.07*) and apneas (0.71±0.05*). Airflow correlated with PSG-airflow during normal breathing (0.29±0.08*), hypopneas (0.47±0.07*) and apneas (0.63±0.09*). (*P Conclusion: Tracheal sounds and movements are promising tools for SA assessment.

An efficient and reliable method to detect drowsiness can reduce accidents and injuries related to drowsy driving. However, existing systems for detecting drowsiness are often of low-resolution, expensive, and dependent on external parameters. Therefore, the goal of this study is to develop a high-resolution and efficient drowsiness detection algorithm using relatively less noisy sleep study data. To this end, we recorded electroencephalogram (EEG) from 53 subjects during a sleep study and leveraged the EEG frequency band changes at sleep onset to develop a model for drowsiness detection. The model devised herein provided a likelihood of wakefulness for 3-s signal segments. By choosing appropriate thresholds of the model output, we have identified three clusters that represent wakefulness, drowsiness, and, sleep. The proposed scheme has been validated using arousals which are cases of alertness and deep sleep segments, cluster quality evaluation metrics, graphical, and statistical analyses. The results presented in this work suggest that spectral properties of EEG can be utilized for high-resolution drowsiness detection in sleep study. Upon its successful validation in a driving study, the proposed model can lead to the development of an efficient drowsy driving monitoring system.

Sleep apnea is a highly prevalent and underdiagnosed sleep disorder characterized by repeated intermittent interruptions to breathing. Sleep apnea severity is measured with the apnea-hypopnea index (AHI), defined as the number of apnea or hypopnea events per hour of sleep. We hypothesize that respiratory related motion features extracted from infrared video can be used to reliably estimate AHI. The 3 feature variables chosen for apneic event estimation, and separately for sleep versus awake estimation, were: the estimated respiratory rate, the magnitude of respiratory movement, and the amount of movements. Leave-one-person-out cross validation on data from 19 participants was used to train and test a random forest binary classifier to detect apneas and hypopneas. Linear regression of the number of estimated events over estimated sleep duration and the total duration of estimated apneic events over estimated sleep duration was used to estimate AHI. Sleeping versus awake segments was estimated with mean ± standard deviation accuracy of 76.0% ± 17.7%. AHI was estimated with correlation coefficient of 0.76 (p < 0.01) to the clinical gold standard AHI. Accuracy of 78.9% was achieved for classifying AHI ≥ 15, with sensitivity of 70.0%, specificity of 88.9%, and precision of 87.5%. Motion features extracted from infrared video are concluded to be suitable for estimation of AHI.

Sleep apnea is a common chronic respiratory disorder which occurs due to the repetitive complete or partial cessations of breathing during sleep. The gold standard assessment of sleep apnea requires full night polysomnography in a sleep laboratory which is expensive, time consuming, and inconvenient. Hence, there is an urgent need for a convenient, robust and wearable monitoring device for screening of sleep apnea. A simple and convenient accelerometer-based portable system is presented to estimate the severity of sleep apnea by analyzing tracheal movements. Respiratory related movements were recorded over the suprasternal notch using a 3D accelerometer. Twenty-one physiological features (7 features, 3 accelerometer channels) were extracted. Performance of three different deep learning models - convolutional neural network, recurrent neural network, and their combination - were evaluated for estimating the apnea hypopnea index (AHI). The estimated AHI is compared to the gold standard polysomnography. In 3-fold cross-validation experiments with 20 participants (9 female, age=47.8±18.0 years, BMI=30.8±4.8, AHI=22.2±21.8 events/hr), we achieved a correlation coefficient between gold standard and estimated values (r-value = 0.84). The proposed system is an accurate, convenient, and portable device suitable for home sleep apnea screening.

Overnight extracellular rostral fluid shifts have been shown to be of importance in patients with fluid-retaining states and are associated with a higher prevalence of sleep apnea. Pulmonary hypertension is frequently associated with right ventricular dysfunction and progressive right ventricular failure, and an increased prevalence of sleep apnea has been described. In light of the importance of fluid shifts in the pathophysiology of sleep apnea, we aimed to explore temporal fluid shifts in patients with pulmonary hypertension with and without sleep apnea. Patients with pulmonary hypertension (WHO Group 1 or 4) had overnight extracellular rostral fluid shift assessment before and a minimum of 3 months after initiation of pulmonary hypertension-specific therapy. Fluid shift measurements of extracellular leg, abdominal, thoracic and neck fluid volumes were performed simultaneously. Twenty-nine patients with pulmonary hypertension (age 55 ± 16 years, 69% female) participated. Sleep apnea was diagnosed in 15 subjects (apnea-hypopnea index 14 [8-27] per hr). There were no significant differences in baseline or overnight leg extracellular rostral fluid, abdominal extracellular rostral fluid, thoracic extracellular rostral fluid or neck extracellular rostral fluid between those with and without sleep apnea. There was a significant inverse correlation between the sleep apnea severity and the overnight change in leg extracellular rostral fluid (r = -0.375, p = 0.049). There were no significant differences detected in overnight extracellular rostral fluid shifts from baseline to follow-up. Treatment-naïve patients with pulmonary hypertension both with and without sleep apnea demonstrate overnight extracellular rostral fluid shifts from the legs into the thorax and neck. Pulmonary hypertension-specific treatment, while significantly improving cardiac haemodynamics, had little impact on nocturnal extracellular rostral fluid shifts or the presence of sleep apnea.

Objective/Background Sleep apnea (SA) is associated with poor outcomes after stroke. The best sleep apnea-related measure to capture this relationship is currently unknown. This measure or its underlying pathophysiology could serve as a treatment target. Patients/Methods Within the population-based Brain Attack Surveillance in Corpus Christi (BASIC) project, the ApneaLink Plus was used to perform sleep apnea tests shortly after ischemic stroke (2010–2015). Functional and cognitive outcomes were measured via in-person interviews 90-days post-stroke. Recurrent stroke was assessed longitudinally through active and passive surveillance procedures. After standardization to allow direct comparisons, adjusted models were built for each ApneaLink Plus measure and each outcome, to assess the effect of 1 standard deviation difference in the measure. Results Among 995 subjects, median age was 67 years (interquartile range: 59, 78) and 52% were women. The respiratory event index had the strongest relationship with functional outcome (mean difference = 0.094, 95% confidence interval (CI): 0.040, 0.147). Desaturations ≤85% were associated with worse functional outcome (mean difference = 0.016, 95% CI: 0.002, 0.030), but desaturations ≤ 90% were not. Obstructive apnea index (OAI) showed the strongest association with cognitive outcome (mean difference = −0.079, 95% CI: −0.162, 0.005), but was not significant. Oxygen desaturation index (ODI) showed the strongest association with recurrent ischemic stroke (hazard ratio = 1.338, 95% CI: 1.016, 1.759). Conclusions Measurements easily obtained from a commonly used home sleep apnea test predicted outcomes differentially. This suggests the possibility of different SA-associated targets (perhaps using strategies more tolerable than standard treatment) based on the outcome of interest.

Sleep apnea (SA) severity is determined by the apnea-hypopnea index (AHI). 85% of patients remain undiagnosed due to the complexity and limited access to polysomnography (PSG). Accordingly, a system for convenient and reliable diagnosis of SA is urgently needed. To address this problem, we aim to develop a wearable technology for AHI estimation using breathing sounds, respiratory related movement and blood oxygen saturation (SaO2). Adults with suspected SA underwent overnight PSG at Toronto Rehabilitations Institute. During PSG, breathing sounds and respiratory related movements were recorded over the suprasternal notch using a microphone and an accelerometer, respectively (the Patch (Figure 1-a)). At first, we calculated the amount of drops in SaO2 from pulse oximeter. Subsequently, energy of breaths and accelerometer were extracted. Features were a) normalized, b) weighted, and c) summed. An event was marked when the summed score was greater than a threshold. Finally, AHI was estimated by dividing the number of events by recording time. Estimated AHI (PatchAHI) was compared to the AHI obtained from PSG (PSGAHI). Data from 50 subjects were investigated. A high correlation was observed between the PatchAHI and PSGAHI (Figure 1-b, r2 = 0.93). Considering a diagnostic cut-off AHI of =15, sensitivity and specificity of 92.3% and 91.8% were achieved, respectively. Our proposed system is easy to operate and comfortable, and it is currently going through clinical trials for final validation.

Background and Purpose— Limited data are available about the relationship between sleep-disordered breathing (SDB) and recurrent stroke and mortality, especially from population-based studies, large samples, or ethnically diverse populations. Methods— In the BASIC project (Brain Attack Surveillance in Corpus Christ), we identified patients with ischemic stroke (2010–2015). Subjects were offered screening for SDB with the ApneaLink Plus device, from which a respiratory event index (REI) score ≥10 defined SDB. Demographics and baseline characteristics were determined from chart review and interview. Recurrent ischemic stroke was identified through active and passive surveillance. Cause-specific proportional hazards models were used to assess the association between REI (modeled linearly) and ischemic stroke recurrence (as the event of interest), and all-cause poststroke mortality, adjusted for multiple potential confounders. Results— Among 842 subjects, the median age was 65 (interquartile range, 57–76), 47% were female, and 58% were Mexican American. The median REI score was 14 (interquartile range, 6–26); 63% had SDB. SDB was associated with male sex, Mexican American ethnicity, being insured, nonsmoking status, diabetes mellitus, hypertension, lower educational attainment, and higher body mass index. Among Mexican American and non-Hispanic whites, 85 (11%) ischemic recurrent strokes and 104 (13%) deaths occurred, with a median follow-up time of 591 days. In fully adjusted models, REI was associated with recurrent ischemic stroke (hazard ratio, 1.02 [hazard ratio for one-unit higher REI score, 95% CI, 1.01–1.03]), but not with mortality alone (hazard ratio, 1.00 [95% CI, 0.99–1.02]). Conclusions— Results from this large population-based study show that SDB is associated with recurrent ischemic stroke, but not mortality. SDB may therefore represent an important modifiable risk factor for poor stroke outcomes.

Induction of fluid overload during sleep in older men causes fluid accumulation in the neck, worsens obstructive sleep apnea (OSA), and reduces sleep efficiency and slow wave sleep. However, it is not clear whether disrupted sleep structure was related to age, fluid accumulation, or to OSA severity as assessed by the apnea-hypopnea index (AHI). We hypothesize that fluid accumulation in the neck is a significant contributor to the sleep structure.Twenty non-obese men, 46 ± 11 years, underwent a daytime sleep study following a night of sleep deprivation. Before and after sleep, neck circumference (NC), upper airway cross-sectional area, and neck fluid volume (NFV) were assessed. Stepwise regression analyses were used to determine factors that contributed to sleep structure, AHI, and arousal frequency. Independent factors were age, NC, ΔNC, ΔNFV, and AHI (excluded for AHI and arousal).Subjects slept for 145 ± 44 minutes with a mean AHI of 26 ± 25. After sleep, NC and NFV increased and the upper airway narrowed (all: p0.001). ΔNC and ΔNFV correlated directly with %N2 and inversely with %N3 sleep. Regression analyses revealed that only ΔNC correlated directly with %N2 sleep (rFluid accumulation in the neck and larger neck circumference are related to impaired sleep structure with reduced %N3 sleep. Fluid accumulation in the neck had stronger contribution to sleep structure than AHI or age.

Objectives Obstructive sleep apnea (OSA) is highly prevalent in patients with fluid-retaining conditions. Using bioimpedance measurements, previous studies have shown that the greater the amount of fluid redistributed from the legs to the neck overnight, the greater the severity of OSA. Our objective was to investigate factors that predispose the development or worsening of OSA in response to experimental fluid overload. Methods Fifteen normotensive and non-obese adult men with and without OSA underwent polysomnography (PSG) during which normal saline was infused intravenously at a minimal rate to keep the vein open (control) or as a bolus of 22 ml/kg body weight (approximately 2 L) in a random order and crossed over after a week. Results and Conclusions Before and after sleep, neck circumference and bioimpedance were measured to calculate neck resistance, reactance, phase angle, and fluid volume. Subjects who experienced more than a twofold increase in apnea–hypopnea index (AHI) or obstructive AHI from control to intervention and had an AHI>10 during intervention were considered susceptible to the development or worsening of OSA. Baseline neck circumference and phase angle before saline infusion were independently associated with increased susceptibility to developing or worsening OSA in response to saline infusion. In non-obese men, a larger neck circumference and bioimpedance phase angle of the neck, which may be associated with larger pharyngeal tissue content, is associated with increased susceptibility for worsening of OSA in response to fluid overloading.

An understanding of the potential mechanisms underlying recurrent upper airway collapse may help anesthesiologists better manage patients in the postoperative period. There is convincing evidence in the sleep medicine literature to suggest that a positive fluid and salt balance can worsen upper airway collapse in patients with obstructive sleep apnea through the redistribution of fluid from the legs into the neck and upper airway while supine, in a process known as "rostral fluid shift." According to this theory, during the day the volume from a fluid bolus or from fluid overload states (i.e., heart failure and chronic kidney disease) accumulates in the legs due to gravity, and when a person lies supine at night, the fluid shifts rostrally to the neck, also owing to gravity. The fluid in the neck can increase the extraluminal pressure around the upper airways, causing the upper airways to narrow and predisposing to upper airway collapse. Similarly, surgical patients also incur large fluid and salt balance shifts, and when recovered supine, this may promote fluid redistribution to the neck and upper airways. In this commentary, we summarize the sleep medicine literature on the impact of fluid and salt balance on obstructive sleep apnea severity and discuss the potential anesthetic implications of excessive fluid and salt volume on worsening sleep apnea.

When lying supine, fluid shifts rostrally from the legs and accumulates in the neck, which is a risk factor for obstructive sleep apnea. The objective of this study was to model neck fluid accumulation using one-time baseline measurements of body fluid, demographics, and anthropometrics. Using bioelectrical impedance, leg and neck fluid volumes (LFV and NFV) were measured continuously and simultaneously. Thirty non-obese adults (13 men) stood quietly for 5 minutes, and then lay supine for 90 minutes while fluid volumes were measured. Neck circumference (NC) was measured before and after the supine period. Results demonstrated that, compared to women, men experienced a greater increase in NC after lying supine. Furthermore, baseline LFV at the onset of lying supine was significantly correlated with ΔLFV (r = 0.44, p = 0.014) and ΔNC (r = 0.51, p = 0.008) after 90 minutes supine. The findings identify that sex and baseline LFV predict both the fluid leaving the legs and increase in NC during recumbency.

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.