Ali Azarbarzin

Automatic and Unsupervised Snore Sound Extraction From Respiratory Sound Signals

In this paper, an automatic and unsupervised snore detection algorithm is proposed. The respiratory sound signals of 30 patients with different levels of airway obstruction were recorded by two microphones: one placed over the trachea (the tracheal microphone), and the other was a freestanding microphone (the ambient microphone). All the recordings were done simultaneously with full-night polysomnography during sleep. The sound activity episodes were identified using the vertical box (V-Box) algorithm. The 500-Hz subband energy distribution and principal component analysis were used to extract discriminative features from sound episodes. An unsupervised fuzzy C-means clustering algorithm was then deployed to label the sound episodes as either snore or no-snore class, which could be breath sound, swallowing sound, or any other noise. The algorithm was evaluated using manual annotation of the sound signals. The overall accuracy of the proposed algorithm was found to be 98.6% for tracheal sounds recordings, and 93.1% for the sounds recorded by the ambient microphone.

Snoring sounds variability as a signature of obstructive sleep apnea

Snoring sounds vary significantly within and between snorers. In this study, the variation of snoring sounds and its association with obstructive sleep apnea (OSA) are quantified. Snoring sounds of 42 snorers with different degrees of obstructive sleep apnea and 15 non-OSA snorers were analyzed. The sounds were recorded by a microphone placed over the suprasternal notch of trachea, simultaneously with polysomnography (PSG) data over the entire night. We hypothesize that snoring sounds vary significantly within a subject depending on the level of obstruction, and thus the level of airflow. We also hypothesize that this variability is associated with the severity of OSA. For each individual, we extracted snoring sound segments from the respiratory recordings, and divided them into three classes: non-apneic, hypopneic, and post-apneic using their PSG information. Several features were extracted from the snoring sound segments, and compared using a nonparametric statistical test. The results show significant shift in the median of features among the snoring sound classes (p<0.00001) of an individual. In contrast to hypopneic and post-apneic classes, the characteristics of snoring sounds did not vary significantly over time in non-apneic class. Therefore, we used the total variation norm of each subject to classify the participants as OSA and non-OSA snorers. The results showed 92.9% sensitivity, 100% specificity and 96.4% accuracy.

Desipramine improves upper airway collapsibility and reduces OSA severity in patients with minimal muscle compensation.

We recently demonstrated that desipramine reduces the sleep-related loss of upper airway dilator muscle activity and reduces pharyngeal collapsibility in healthy humans without obstructive sleep apnoea (OSA). The aim of the present physiological study was to determine the effects of desipramine on upper airway collapsibility and apnoea–hypopnea index (AHI) in OSA patients. A placebo-controlled, double-blind, randomised crossover trial in 14 OSA patients was performed. Participants received treatment or placebo in randomised order before sleep. Pharyngeal collapsibility (critical collapsing pressure of the upper airway (Pcrit)) and ventilation under both passive (V′0,passive) and active (V′0,active) upper airway muscle conditions were evaluated with continuous positive airway pressure (CPAP) manipulation. AHI was quantified off CPAP. Desipramine reduced active Pcrit (median (interquartile range) −5.2 (4.3) cmH2O on desipramine versus −1.9 (2.7) cmH2O on placebo; p=0.049) but not passive Pcrit (−2.2 (3.4) versus −0.7 (2.1) cmH2O; p=0.135). A greater reduction in AHI occurred in those with minimal muscle compensation (defined as V′0,active−V′0,passive) on placebo (r=0.71, p=0.009). The reduction in AHI was driven by the improvement in muscle compensation (r=0.72, p=0.009). In OSA patients, noradrenergic stimulation with desipramine improves pharyngeal collapsibility and may be an effective treatment in patients with minimal upper airway muscle compensation. Desipramine administered before sleep reduces pharyngeal collapsibility in patients with obstructive sleep apnoea http://ow.ly/1auA302CL8F

Arousal Intensity is a Distinct Pathophysiological Trait in Obstructive Sleep Apnea.

STUDY OBJECTIVES Arousals from sleep vary in duration and intensity. Accordingly, the physiological consequences of different types of arousals may also vary. Factors that influence arousal intensity are only partly understood. This study aimed to determine if arousal intensity is mediated by the strength of the preceding respiratory stimulus, and investigate other factors mediating arousal intensity and its role on post-arousal ventilatory and pharyngeal muscle responses. METHODS Data were acquired in 71 adults (17 controls, 54 obstructive sleep apnea patients) instrumented with polysomnography equipment plus genioglossus and tensor palatini electromyography (EMG), a nasal mask and pneumotachograph, and an epiglottic pressure sensor. Transient reductions in CPAP were delivered during sleep to induce respiratory-related arousals. Arousal intensity was measured using a validated 10-point scale. RESULTS Average arousal intensity was not related to the magnitude of the preceding respiratory stimuli but was positively associated with arousal duration, time to arousal, rate of change in epiglottic pressure and negatively with BMI (R2 > 0.10, P ≤ 0.006). High (> 5) intensity arousals caused greater ventilatory responses than low (≤ 5) intensity arousals (10.9 [6.8-14.5] vs. 7.8 [4.7-12.9] L/min; P = 0.036) and greater increases in tensor palatini EMG (10 [3-17] vs. 6 [2-11]%max; P = 0.031), with less pronounced increases in genioglossus EMG. CONCLUSIONS Average arousal intensity is independent of the preceding respiratory stimulus. This is consistent with arousal intensity being a distinct trait. Respiratory and pharyngeal muscle responses increase with arousal intensity. Thus, patients with higher arousal intensities may be more prone to respiratory control instability. These findings are important for sleep apnea pathogenesis.

Respiratory Flow–Sound Relationship During Both Wakefulness and Sleep and Its Variation in Relation to Sleep Apnea

Tracheal respiratory sound analysis is a simple and non-invasive way to study the pathophysiology of the upper airway and has recently been used for acoustic estimation of respiratory flow and sleep apnea diagnosis. However in none of the previous studies was the respiratory flow–sound relationship studied in people with obstructive sleep apnea (OSA), nor during sleep. In this study, we recorded tracheal sound, respiratory flow, and head position from eight non-OSA and 10 OSA individuals during sleep and wakefulness. We compared the flow–sound relationship and variations in model parameters from wakefulness to sleep within and between the two groups. The results show that during both wakefulness and sleep, flow–sound relationship follows a power law but with different parameters. Furthermore, the variations in model parameters may be representative of the OSA pathology. The other objective of this study was to examine the accuracy of respiratory flow estimation algorithms during sleep: we investigated two approaches for calibrating the model parameters using the known data recorded during either wakefulness or sleep. The results show that the acoustical respiratory flow estimation parameters change from wakefulness to sleep. Therefore, if the model is calibrated using wakefulness data, although the estimated respiratory flow follows the relative variations of the real flow, the quantitative flow estimation error would be high during sleep. On the other hand, when the calibration parameters are extracted from tracheal sound and respiratory flow recordings during sleep, the respiratory flow estimation error is less than 10%.

Estimation of pharyngeal collapsibility during sleep by peak inspiratory airflow.

Objectives Pharyngeal critical closing pressure (Pcrit) or collapsibility is a major determinant of obstructive sleep apnea (OSA) and may be used to predict the success/failure of non-continuous positive airway pressure (CPAP) therapies. Since its assessment involves overnight manipulation of CPAP, we sought to validate the peak inspiratory flow during natural sleep (without CPAP) as a simple surrogate measurement of collapsibility. Methods Fourteen patients with OSA attended overnight polysomnography with pneumotachograph airflow. The middle third of the night (non-rapid eye movement sleep [NREM]) was dedicated to assessing Pcrit in passive and active states via abrupt and gradual CPAP pressure drops, respectively. Pcrit is the extrapolated CPAP pressure at which flow is zero. Peak and mid-inspiratory flow off CPAP was obtained from all breaths during sleep (excluding arousal) and compared with Pcrit. Results Active Pcrit, measured during NREM sleep, was strongly correlated with both peak and mid-inspiratory flow during NREM sleep (r = -0.71, p < .005 and r = -0.64, p < .05, respectively), indicating that active pharyngeal collapsibility can be reliably estimated from simple airflow measurements during polysomnography. However, there was no significant relationship between passive Pcrit, measured during NREM sleep, and peak or mid-inspiratory flow obtained from NREM sleep. Flow measurements during REM sleep were not significantly associated with active or passive Pcrit. Conclusions Our study demonstrates the feasibility of estimating active Pcrit using flow measurements in patients with OSA. This method may enable clinicians to estimate pharyngeal collapsibility without sophisticated equipment and potentially aid in the selection of patients for non- positive airway pressure therapies.

Intra-subject variability of snoring sounds in relation to body position, sleep stage, and blood oxygen level.

In a multidimensional feature space, the snoring sounds can extend from a very compact cluster to highly distinct clusters. In this study, we investigated the cause of snoring sound’s variation within the snorers. It is known that a change in body position and sleep stage can affect snoring during sleep but it is unclear whether positional, sleep state, and blood oxygen level variations cause the snoring sounds to have different characteristics, and if it does how significant that effect would be. We extracted 12 characteristic features from snoring sound segments of 57 snorers and transformed them into a 4-D feature space using principal component analysis (PCA). Then, they were grouped based on the body position (side, supine, and prone), sleep stage (NREM, REM, and Arousal), and blood oxygen level (Normal and Desaturation). The probability density function of the transformed features was calculated for each class of categorical variables. The distance between the class-densities were calculated to determine which of these parameters affects the snoring sounds significantly. Analysis of Variance (ANOVA) was run for each categorical variable. The results show that the positional change has the highest effect on the snoring sounds; it results in forming distinct clusters of snoring sounds. Also, sleep state and blood oxygen level variation have been found to moderately affect the snoring sounds.

Phenotyping Pharyngeal Pathophysiology using Polysomnography in Patients with Obstructive Sleep Apnea

Rationale: Therapies for obstructive sleep apnea (OSA) could be administered on the basis of a patients own phenotypic causes (“traits”) if a clinically applicable approach were available. Objectives: Here we aimed to provide a means to quantify two key contributors to OSA—pharyngeal collapsibility and compensatory muscle responsiveness—that is applicable to diagnostic polysomnography. Methods: Based on physiological definitions, pharyngeal collapsibility determines the ventilation at normal (eupneic) ventilatory drive during sleep, and pharyngeal compensation determines the rise in ventilation accompanying a rising ventilatory drive. Thus, measuring ventilation and ventilatory drive (e.g., during spontaneous cyclic events) should reveal a patients phenotypic traits without specialized intervention. We demonstrate this concept in patients with OSA (N = 29), using a novel automated noninvasive method to estimate ventilatory drive (polysomnographic method) and using “gold standard” ventilatory drive (intraesophageal diaphragm EMG) for comparison. Specialized physiological measurements using continuous positive airway pressure manipulation were employed for further comparison. The validity of nasal pressure as a ventilation surrogate was also tested (N = 11). Measurements and Main Results: Polysomnography‐derived collapsibility and compensation estimates correlated favorably with those quantified using gold standard ventilatory drive (R = 0.83, P < 0.0001; and R = 0.76, P < 0.0001; respectively) and using continuous positive airway pressure manipulation (R = 0.67, P < 0.0001; and R = 0.64, P < 0.001; respectively). Polysomnographic estimates effectively stratified patients into high versus low subgroups (accuracy, 69‐86% vs. ventilatory drive measures; P < 0.05). Traits were near‐identical using nasal pressure versus pneumotach (N = 11, R ≥ 0.98, both traits; P < 0.001). Conclusions: Phenotypes of pharyngeal dysfunction in OSA are evident from spontaneous changes in ventilation and ventilatory drive during sleep, enabling noninvasive phenotyping in the clinic. Our approach may facilitate precision therapeutic interventions for OSA.

Contribution of Arousal from Sleep to Postevent Tachycardia in Patients with Obstructive Sleep Apnea

STUDY OBJECTIVES Heart rate increases after obstructive events in patients with obstructive sleep apnea (OSA). This response is generally attributed to arousal from sleep. Opening of the obstructed airway, however, is associated with ventilatory and hemodynamic changes that could result in physiologic responses unrelated to arousal. Our objective was to determine the contribution of these physiologic responses to postevent tachycardia. DESIGN Analysis of data obtained during previous research protocols. SETTING Academic sleep laboratory. PARTICIPANTS Twenty patients with severe OSA. INTERVENTIONS Patients were placed on a continuous positive airway pressure (CPAP) device. CPAP was reduced during sleep to different levels (dial-downs), producing obstructive events of varying severity. Some dial-downs with severe obstruction were maintained until spontaneous airway opening. In others, CPAP was increased after three obstructed breaths, terminating the events approximately 10 sec before spontaneous termination in long dial-downs. MEASUREMENT AND RESULTS Beat-by-beat heart rate (HR) was measured for 20 sec following airway opening. Spontaneous opening during sustained dial-downs occurred 21.9 ± 8.4 sec after dial-down, was associated with arousal, and resulted in the greatest postevent tachycardia (7.8 ± 4.0 min(-1)). However, deliberate termination of events (12.2 ± 2.6 sec after dial-down) was also followed by tachycardia that, in the absence of cortical arousal, showed a dose-response behavior, increasing with severity of obstruction and without apparent threshold. ΔHR following deliberately brief, severe obstruction (3.8 ± 3.0 min(-1)) was approximately half the ΔHR that followed spontaneous opening of equally severe obstructions despite the shorter duration and absence of cortical arousal. CONCLUSIONS Postevent tachycardia is due in large part to physiologic (arousal-unrelated) responses that occur upon relief of obstruction.

A comparison between recording sites of snoring sounds in relation to upper airway obstruction

This paper presents the results of our study on investigating the acoustical properties of snoring sounds (SS) recorded by two microphones (one over trachea and one hung in the air within 30-50 cm away from the subject) in relation to sleep apnea. Several features were extracted from SS segments of 50 snorers with different Apnea-Hypopnea Index (AHI). We used an optimal subset of the sound features to cluster the SS segments into two clusters (A and B). Then, the number of SS segments in cluster A was calculated and normalized by the total number of SS segments for each subject, resulting in 50×1 vector R. A correlation analysis was run between AHI and R. The results show a difference in acoustical properties of the tracheal and ambient snoring sounds and their ability to distinguish two types of snoring; the ambient snoring sounds are not as characteristic as tracheal snoring sounds.