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Background Overnight fluid shift from the legs to the neck may narrow the upper airway and contribute to obstructive sleep apnea (OSA) pathogenesis. We hypothesized that below-the-knee compression stockings will decrease OSA severity in a general OSA population by decreasing daytime leg fluid accumulation and overnight fluid shift and increasing upper-airway size. Methods Patients with OSA (apnea–hypopnea index ≥10) were randomized to wear compression stockings during the daytime or to a control group for 2 weeks. Overnight polysomnography with measurement of leg and neck fluid volumes and upper-airway cross-sectional area before and after sleep was performed at baseline and follow-up. The primary outcome was change in the apnea–hypopnea index. Results Twenty-two patients randomized to compression stockings and 23 to control completed the study. The apnea–hypopnea index decreased significantly more in the compression stockings than in the control group (from 32.4 ± 20.0 to 23.8 ± 15.5 vs. from 31.2 ± 25.0 to 30.3 ± 23.8, p = 0.042), in association with a significantly greater reduction in the overnight decrease in leg fluid volume ( p = 0.028), and a significantly greater increase in morning upper-airway cross-sectional area ( p = 0.006). Overnight change in neck fluid volume was unchanged. Conclusion These observations suggest that in, a general OSA population, below-the-knee compression stockings decrease OSA severity modestly via attenuation of overnight fluid shift and consequent upper-airway dilatation.

While supine, fluid moves from the legs and accumulates in the chest and neck. However, patterns of rostral fluid shift are not clear. Furthermore, real-time measurement of neck fluid volume has not been investigated. The objective of this study was to investigate the dynamics of rostral fluid shift in men and women. We developed a bioelectrical impedance system to measure leg, abdominal, thoracic and neck fluid volumes (LFV, AFV, TFV, NFV) continuously. Forty healthy non-obese adults (20 men) lay supine for 90 min while fluid volumes were measured. After 90 min, a similar volume of fluid shifted out of the legs in both sexes (p = 0.079), but men accumulated more fluid in their thorax (63 ± 6 vs. 44 ± 11 ml, p = 0.016) and neck (17 ± 2 vs. 14 ± 1 ml, p = 0.029) than women. In both sexes, the increase in NFV caused a significant increase in neck circumference, which was greater in men (p = 0.009). Furthermore, 80% of rostral fluid shift would occur in the first 2 h of lying supine. These results suggest that greater fluid shift into the thorax and neck may contribute to the higher prevalence of sleep apnea in men than in women.

Inspiratory flow limitation is a breathing pattern during sleep caused by upper airway (UA) narrowing that occurs during snoring and various degrees of obstructive sleep apnea (OSA). Clinical examination of flow limitation relies on identifying patterns of airflow contour, however this process is subjective and lacks physiological evidence of UA narrowing. Our objective is to derive the temporal features of nasal airflow contour that characterize flow limitation. The features that correlate with UA narrowing can be used to develop machine learning classifiers to detect flow limitation with physiological support.Sixteen healthy adult men underwent full daytime polysomnography where the nasal airflow was recorded. Before and after sleep, we measured UA anatomical parameters including neck circumference (NC) and upper-airway cross-sectional area (UA-XSA). We extracted various temporal features of airflow and investigated their relationships with the UA anatomical parameters.We found that three features were correlated with the anatomical parameters associated with UA narrowing: deviation index vs. baseline UA-XSA (r = -0.67, p = 0.01), peak amplitude variability vs. baseline UA-XSA (r = -0.69, p 0.01), peak amplitude variability vs. ΔNC (r = 0.74, p 0.01) and peak number vs. baseline UA-XSA (r = -0.54, p = 0.04).Temporal features of airflow were associated with UA narrowing. Future studies could utilize the features to develop classifiers to detect flow limitation and assess the severity of breathing disorders during sleep in high-risk populations such as pregnant women and children.

The present study tests the hypothesis that individuals who develop sleep apnea due to saline infusion during sleep will experience autonomic dysregulation, compared to those without saline-induced sleep apnea. Twenty-one normotensive, nonobese (mean BMI 24.5 kg/m 2 ) men (mean age 37 years) underwent a sleep study. Through random assignment, participants were infused with saline during sleep at the minimum rate to keep the vein open (control) or as a bolus of 22 ml/kg body weight (intervention). Participants crossed over to the other study arm one week later. Heart rate variability (HRV) measurements were computed from continuous electrocardiographic recordings pre- and post-saline infusion in the intervention arm. HRV measures computed were: standard deviation of the RR interval (SDRR), and low (LF) and high frequency (HF) power. Eight participants had increased apnea-hypopnea index by saline infusion (AHI+), and 13 had no change in apnea-hypopnea index by saline infusion (AHI−). Changes in LF power from pre- to post-infusion were not different between the groups. However, while pre-saline infusion values were similar, post-saline infusion values of SDRR and HF power were lower in the AHI+, compared to the AHI− group (Figure 1, P

STUDY OBJECTIVES: Snoring sounds are generated by the vibration of pharyngeal tissue due to the upper airway narrowing. While recorded by a microphone placed over the neck, snoring can pass through the pharyngeal tissue surrounding the upper airway. Thus, changes in the pharyngeal tissue content may change the acoustic properties of the snoring sounds. Rostral fluid shift and the consequent increases in neck fluid volume (NFV) and neck circumference (NC) can increase pharyngeal tissue mass. Therefore, the goal of this study was to investigate the relationship between increases in pharyngeal tissue mass, as assessed by increased NFV and NC, and snoring sounds features. METHODS: We obtained data from a previous study where 20 males who were not obese participated in a daytime polysomnography and their NC and NFV were measured before and after sleep. During sleep, snoring sounds were recorded with a microphone placed over the neck. Spectral centroid of the snoring sounds was estimated. Then, the first five snoring segments were selected from the first and last 30 minutes of stage N2 sleep. RESULTS: We found a significant decrease in the snoring spectral centroid from the beginning to end of sleep. We also found that spectral centroid from the end of sleep in frequency ranges below 200 Hz was inversely correlated with the increases in NFV and NC from before to after sleep. CONCLUSIONS: These results suggest that snoring spectral centroid can be used as a noninvasive and convenient method to assess variations in the pharyngeal tissue mass. CITATION: Saha S, Moussavi Z, Hadi P, Bradley TD, Yadollahi A. Effects of increased pharyngeal tissue mass due to fluid accumulation in the neck on the acoustic features of snoring sounds in men. J Clin Sleep Med. 2018;14(10):1653–1660.

Leg fluid accumulation during sedentary behaviours such as sitting can lead to leg edema and associated adverse health consequences. This study investigates the use calf muscle electrical stimulation (ES) to reduce seated leg fluid accumulation. Thirteen non-obese, normotensive men (mean age 51 yr.) with sleep apnea were enrolled in the study. Participants first lay supine for 30 minutes to equalize fluid distribution and then sat for 150 minutes. While seated, participants received either active or sham ES of the calf muscles, according to random assignment. Participants returned one-week later to cross over to the other study condition. Leg fluid was measured continuously while sitting using the bioelectrical impedance method. Fluid accumulation in the leg was reduced by more than 40% using active ES, compared to sham ES (∆ = 51.9 ± 8.8 ml vs. ∆ = 91.5 ± 8.9 ml, P

Snoring is common in the general population and the irregularity could lead to the presence of Obstructive sleep apnea. Diagnosis of OSA could therefore be made by snoring sound analysis. However, there is still a shortage of robust methods to automatically detect snoring sounds without the need to calibrate for every individual. In this paper, a novel method based on neural network is proposed to classify breathing sound episodes from snoring and non-snoring sound segments. Our snore detection algorithm was applied to the tracheal sounds of nine individuals with different OSA severities. On the testing dataset, the classifier achieved a sensitivity and specificity of 95.9% and 97.6% respectively. Our results indicate that using such a method could help to detect snoring sounds with high accuracy which would be useful in the diagnosis of sleep apnea.

STUDY OBJECTIVES Obstructive sleep apnea (OSA) is commoner in patients with fluid-retaining states than in those without fluid retention, in men than in women, and worsens with aging. In men, OSA severity is related to the amount of fluid shifting out of the legs overnight, but a cause-effect relationship is not established. Our objective was to test the hypothesis that mimicking fluid overload during sleep would increase severity of OSA more in older (≥ 40 years) than in younger men (< 40 years). DESIGN Randomized, single-blind, double crossover study. SETTING Research sleep laboratory. PATIENTS OR PARTICIPANTS Seven older and 10 younger men with non-severe or no sleep apnea, matched for body mass index. INTERVENTIONS During the control arm, normal saline was infused to keep the vein open. During intervention, subjects received an intravenous bolus of normal saline (22 mL/kg body weight) after sleep onset while they were wearing compression stockings to prevent fluid accumulation in the legs. MEASUREMENTS AND RESULTS Compared to younger men, infusion of similar amounts of saline in older men caused a greater increase in neck circumference (P < 0.05) and in the AHI (32.2 ± 22.1 vs. 2.2 ± 7.1, P = 0.002). CONCLUSIONS Older men are more susceptible to the adverse effects of intravenous fluid loading on obstructive sleep apnea severity than younger men. This may be due to age-related differences in the amount of fluid accumulating in the neck or upper airway collapsibility in response to intravenous fluid loading. These possibilities remain to be tested in future studies.

Recently we showed that fluid accumulation in the neck can narrow the upper airway (UA) and increase its collapsibility, which may exacerbate obstructive sleep apnea (OSA). However, the available methods for measuring neck fluid volume (NFV) are inconvenient and expensive. Narrowing of the UA due to fluid accumulation could change acoustic characteristics of respiratory sounds. In this study, we developed a novel approach for non-invasive estimation of NFV from acoustic measurements. Twenty-eight healthy subjects lay awake and supine for 90 min while NFV and tracheal sounds were measured simultaneously using bioimpedance and a microphone, respectively. Sets of tracheal sound features were calculated in time and frequency domains and were reduced using methods based on regression and minimum-redundancy-maximum-relevance. The resulting feature sets were applied to a multi-linear regression and a mixture-density neural network to estimate NFV. Our results show very small relative estimation errors of 1.25 and 3.23%, based on the regression and neural network methods, respectively. These results support the practical application of this technology in diagnosing fluid accumulation in the neck and its possible contributions to the pathogenesis of OSA.

Obstructive Sleep apnea can be caused by fluid shift from the legs to the neck that narrows the upper airway (UA) and contributes to changes in tracheal sound. Tracheal sound is generated from the turbulent airflow in the pharynx and respiratory airways and it has recently been used to estimate increases in neck fluid volume (NFV). However, tracheal sound is also highly variable among people, especially across the sexes. In this paper, a novel method is proposed to select tracheal sound features towards estimating NFV in men and women separately. To validate this method, it was applied to the tracheal sound data of 28 healthy individuals. Our proposed feature selection algorithm is based on sparse representations and incorporates NFV to maximize the relevance of selected features. This feature selection eliminates the dependence of the previous methods on calibrating the model for every individual. Two models, regression and Kalman filters, are then used to estimate NFV from selected features. Kalman filter obtains the highest performance, estimating NFV with more than 90% accuracy in both men and women. This algorithm can be used to develop non-invasive acoustic technologies to investigate the effects of fluid on UA anatomy in general applications. These results could be used to develop convenient devices to monitor the neck edema and its contribution to sleep apnea severity in fluid retaining patients such as heart or renal failure.

The accumulation of fluid in the legs due to sedentariness can be a health risk in extreme cases. Negative health impacts associated with leg fluid accumulation include leg edema and risk of blood clots. Furthermore, fluid accumulating in the legs is accompanied by fluid shift into the upper body which is also associated with health risks such as: increased blood pressure when lying down, respiratory problems in people with heart failure, and increased sleep apnea. Understanding the pattern by which fluid accumulates in the legs can aid in the development of devices for reducing leg fluid accumulation. The purpose of this study was to characterize the time course of fluid accumulation over a two-and-half-hour seated period. Non-obese participants with sleep apnea and no other co-morbidities were included in the sample as part of a larger study. Leg fluid was measured continuously using a method of bioelectrical impedance. Participants were first asked to lie supine for 30 minutes as a washout, and then sat with their legs still for two and a half hours. The main finding of this study is that the pattern of leg fluid accumulation differed in the first 45 minutes compared to the latter 105 minutes. In the first 45 minutes, fluid accumulated according to first order exponential function. In the latter period, fluid accumulated according to a linear function. The initial exponential accumulation is likely due to the large increase in capillary pressure caused by rapid blood flow into the legs due to gravity, leading to substantial filtration of blood plasma into the tissue spaces. The latter linear portion likely represents continued slow filtration of fluid out of the vasculature and into the tissue spaces. This is the first study to show that fluid accumulation in the legs is a combination of an exponential and linear functions. The linear increase identifies that there is no foreseeable point in which leg fluid stops accumulating while sitting for prolonged periods.

Fluid shift from the legs and into the neck during sleep has been shown to increase tissue pressure around the upper airway and increase sleep apnea severity. Recently, acute fluid overload via saline infusion during sleep was shown to increase sleep apnea severity in an older men (≥ 40 years of age) but not the younger men (;40 years of age). The purpose of this study was to estimate the changes in the autonomic nervous system response to saline infusion in the younger and older men using well studied time- and frequency domain heart rate variability metrics (HRV). Eighteen healthy men (10 in the younger group and 8 in the older group) slept for up to 3 hours during the day while ≈ 2L of saline was intravenously injected during sleep. An electrocardiogram was collected continuously during the sleep period. Two five minute segments of stage 1 or 2 sleep were selected for analysis of HRV: one before saline infusion and one after completion of saline infusion. Time- and frequency domain metrics of HRV were used to characterize autonomic nervous system response. Results generally showed increased HRV as measured by time-domain statistical measures in the younger men after saline infusion. In the frequency domain, the change in high frequency power from pre- to post-saline infusion was significantly greater in the younger compared to the older men. In addition, there was a borderline significant trend showing an increase in HF power from pre- to post-saline infusion in the younger, but not the older men. These results suggest that only in the younger men and not the older men, parasympathetic nervous activity increased in response to acute fluid overload. This might be one of the mechanisms that increased the severity of OSA in older and not the younger men.

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%.

Tracheal sound average power is directly related to the breathing flow rate and recently it has attracted considerable attention for acoustical flow estimation. However, the flow-sound relationship is highly variable among people and it also changes for the same person at different flow rates. Hence, a robust model capable of estimating flow from tracheal sounds at different flow rates in a large group of individuals does not exist. In this paper, a model is proposed to estimate respiratory flow from tracheal sounds. The proposed model eliminates the dependence of the previous methods on calibrating the model for every individual and at different flow rates. To validate the model, it was applied to the respiratory sound and flow data of 93 healthy individuals. We investigated the statistical correlation between the model parameters and anthropometric features of the subjects. The results have shown that gender, height, and smoking are the most significant factors that affect the model parameters. Hence, we grouped nonsmoker subjects into four groups based on their gender and height. The average of model parameters in each group was defined as the group-calibrated model parameters. These models were applied to estimate flow from data of subjects within the same group and in the other groups. The results show that flow estimation error based on the group-calibrated model is less than 10%. The low estimation errors confirm the possibility of defining a general flow estimation model for subjects with similar anthropometric features with no need for calibrating the model parameters for every individual. This technique simplifies the acoustical flow estimation in general applications including sleep studies and patients' screening in health care facilities.

Introduction Nocturnal worsening of asthma symptoms is a common feature of asthma. Physical exercise training improves general asthma control; however, there is no evidence showing the effects of physical exercise on nocturnal asthma symptoms. Indeed, asthma patients with daytime and nighttime symptoms are physiologically different, and thus the effects of physical exercise on asthma may also be different in these two groups. The objective of this systematic review is to explore the effects of physical exercise on nocturnal asthma symptoms. Methods Searches were conducted in MEDLINE, Embase, Cochrane Central Register of Controlled Trials, CINAHL and SPORTdiscus (last search on November 2017). Authors from studies that did not report nocturnal symptoms but used questionnaires and/or diaries were contacted for detailed information. Studies that provided results on nocturnal symptoms before and after physical activity intervention were included. Prevalence of nocturnal symptoms was calculated for each study from the percentage of study participants with nocturnal symptoms before and after intervention. Results Eleven studies were included (5 with children and 6 with adults). The prevalence of nocturnal symptoms at baseline ranged from 0% to 63% among children and from 50-73% among adults. In children and adults with nocturnal asthma, aerobic physical exercise reduced the prevalence and frequency of nocturnal symptoms. Conclusions Aerobic physical exercise improves nocturnal asthma in children and adults by reducing the prevalence and frequency of nocturnal symptoms. Physical exercise training could be used with conventional treatments to improve quality of life and asthma control in patients with nocturnal worsening of asthma.

Overnight fluid shift from the legs to the neck and lungs may contribute to the pathogenesis of obstructive sleep apnoea (OSA) and central sleep apnoea (CSA). We hypothesised that exercise training will decrease the severity of OSA and CSA in patients with coronary artery disease (CAD) by decreasing daytime leg fluid accumulation and overnight rostral fluid shift.Patients with CAD and OSA or CSA (apnoea–hypopnoea index >15 events per h) were randomised to 4 weeks of aerobic exercise training or to a control group. Polysomnography, with measurement of leg, thoracic and neck fluid volumes and upper-airway cross-sectional area (UA-XSA) before and after sleep, was performed at baseline and follow-up.17 patients per group completed the study. Apnoea–hypopnoea index decreased significantly more in the exercise group than in the control group (31.1±12.9 to 20.5±9.4versus28.1±13.5 to 27.0±15.1 events per h, p=0.047), in association with a greater reduction in the overnight change in leg fluid volume (579±222 to 466±163versus453±164 to 434±141 mL, p=0.04) and by a significantly greater increase in the overnight change in UA-XSA in the exercise group (p=0.04).In patients with CAD and sleep apnoea, exercise training decreases sleep apnoea severityviaattenuation of overnight fluid shift and an increase in UA-XSA.

Obstructive sleep apnea (OSA) is a common disorder in adults characterized by repetitive collapse of the pharynx. OSA prevalence increases in fluid retaining patients such as those with heart or renal failure, and worsens with overnight fluid accumulation in the neck. The objective of this study was to develop a new method of measuring changes in intracellular water (ICW) in the neck, and investigate metrics that represent total neck impedance and their relationship to sleep apnea severity. In 18 non-obese men, neck fluid volume (NFV) was measured before and after sleep using bioelectrical impedance at 50 kHz. For each participant, resistance and reactance was extracted from the impedance measurements. A model was developed to estimate the cell membrane capacitance which could represent changes in intracellular fluid in the neck. OSA severity was assessed using polysomnography to estimate the apnea-hypopnea index (AHI) as well as the obstructive AHI (OAHI). Our results showed a strong correlation between the changes in NFV from before to after sleep with the changes in cell membrane capacitance from before to after sleep, indicating an increase in ICW in the neck during sleep. Using linear stepwise regression we were also able to develop models to accurately predict AHI and OAHI using baseline anthropometric and bioimpedance measurements. These promising results demonstrate that non-invasive measurements of bioimpedance can be used to develop a novel biomarker to model sleep apnea severity, and assess patients at high risk of OSA.

A non-contact vision-based system is presented for continuous respiratory rate monitoring. The system identifies feature points in a video feed and tracks them over time. Two methods are presented for comparison - a method which uses principal component analysis (PCA) and a simple averaging approach. These methods condense the feature point trajectories into a compact set of representative signals. The signal which most closely resembles an expected respiratory trace is selected based on spectral analysis. System performance is assessed by comparing the estimated respiratory rate to the rate determined via inductance plethysmogram. The system was evaluated on 5 participants in 4 simulated sleep scenarios. Accuracies of within 1 breath/minute were achieved for more than 97% of the recorded time in all scenarios. The proposed system is accurate, cost-effective, and simple, making it a suitable candidate for at-home installation.

As in heart failure, obstructive and central sleep apnoea (OSA and CSA, respectively) are common in end-stage renal disease. Fluid overload characterises end-stage renal disease and heart failure, and in heart failure plays a role in the pathogenesis of OSA and CSA. We postulated that in end-stage renal disease patients, those with sleep apnoea would have greater fluid volume overload than those without.End-stage renal disease patients on thrice-weekly haemodialysis underwent overnight polysomnography on a nondialysis day to determine their apnoea–hypopnoea index (AHI). Extracellular fluid volume of the total body, neck, thorax and right leg were measured using bioelectrical impedance.28 patients had an AHI ≥15 (sleep apnoea group; OSA:CSA 21:7) and 12 had an AHI These findings support a role for fluid overload in the pathogenesis of both OSA and CSA in end-stage renal disease.

Obstructive sleep apnea (OSA) is a common respiratory disorder among adults. Recently we have shown that sedentary lifestyle causes an increase in diurnal leg fluid volume (LFV), which can shift into the neck at night when lying down to sleep and increase OSA severity. The purpose of this work was to investigate various metrics that represent baseline fluid retention in the legs and examine their correlation with neck fluid volume (NFV) and to develop a robust model for predicting fluid accumulation in the neck. In 13 healthy awake non-obese men, LFV and NFV were recorded continuously and simultaneously while standing for 5 minutes and then lying supine for 90 minutes. Simple regression was used to examine correlations between baseline LFV, baseline neck circumference (NC) and change in LFV with the outcome variables: change in NC (ΔNC) and in NFV (ΔNFV90) after lying supine for 90 minutes. An exhaustive grid search was implemented to find combinations of input variables which best modeled outcomes. We found strong positive correlations between baseline LFV (supine and standing) and ΔNFV90. Models developed for predicting ΔNFV90 included baseline standing LFV, baseline NC combined with change in LFV after lying supine for 90 minutes. These correlations and the developed models suggest that a greater baseline LFV might contribute to increased fluid accumulation in the neck. These results give more evidence that sedentary lifestyle might play a role in the pathogenesis of OSA by increasing the baseline LFV. The best models for predicting ΔNC include baseline LFV and NC; they improved accuracies of estimating ΔNC over individual predictors, suggesting that a combination of baseline fluid metrics is a good predictor of the change in NC while lying supine. Future work is aimed at adding additional baseline demographic features to improve model accuracy and eventually use it as a screening tool to predict severity of OSA prior to sleep.

Obstructive sleep apnea (OSA) is a common disorder due to recurrent collapse of the upper airway (UA) during sleep that increases the risk for several cardiovascular diseases. Recently, we showed that nocturnal fluid accumulation in the neck can narrow the UA and predispose to OSA. Our goal is to develop non-invasive methods to study the pathogenesis of OSA and the factors that increase the risks of developing it. Respiratory sound analysis is a simple and non-invasive way to study variations in the properties of the UA. In this study we examine whether such analysis can be used to estimate the amount of neck fluid volume and whether fluid accumulation in the neck alters the properties of these sounds. Our acoustic features include estimates of formants, pitch, energy, duration, zero crossing rate, average power, Mel frequency power, Mel cepstral coefficients, skewness, and kurtosis across segments of sleep. Our results show that while all acoustic features vary significantly among subjects, only the variations in respiratory sound energy, power, duration, pitch, and formants varied significantly over time. Decreases in energy and power over time accompany increases in neck fluid volume which may indicate narrowing of UA and consequently an increased risk of OSA. Finally, simple discriminant analysis was used to estimate broad classes of neck fluid volume from acoustic features with an accuracy of 75%. These results suggest that acoustic analysis of respiratory sounds might be used to assess the role of fluid accumulation in the neck on the pathogenesis of OSA.

Respiratory sound analysis is a simple and noninvasive way to study the pathophysiology of the upper airway (UA). Recently, it has been used to diagnose partial or complete UA collapse in patients with obstructive sleep apnea (OSA). In this study, we investigated whether uid accumulation in the neck alters the properties of respiratory sounds in temporal and spectral domains and whether the respiratory sounds analysis can be used to monitor variations in the physiology of the UA, as re ected by UA resistance (R UA ). We recorded respiratory sounds and R UA from 19 individuals while awake. We applied lower body positive pressure (LBPP) to shift uid out of the legs and into the neck, which increased R UA . We calculated !rst and second formants and energy of inspiratory sound segments. Our results show that during both control (no LBPP) and LBPP arms of the study, the extracted features were different for the sound segments corresponding to low and high R UA . Also, the features were different during control and LBPP arms of the study. With the application of support vector machine (SVM) based classi!er, we were able to classify the sound segments into two groups of high/low resistance during control and LBPP arms and into two groups of control/LBPP when including all sound segments. The accuracies of non-linear SVM classi!er were 74.5 ± 19.5%, 75.0 ± 15.4% and 77.1 ± 12.3% for the control arm, LBPP arm and between the arms, respectively. We also showed that during the LBPP arm, the variations in !rst formant of the sound segments corresponding to low and high R UA was much less than during the control arm. This indicates that with application of LBPP and accumulation of uid in the neck, there are less variations in the morphology of the UA in response to changes in R UA , than during the control arm. These results indicate that acoustic analysis of respiratory sounds can be used to investigate physiology of the UA and how interventions can alter UA properties.

Tracheal respiratory sound analysis is a simple and non-invasive way to study the pathophysiology of the upper airways; it has recently been used for acoustical flow estimation and sleep apnea diagnosis. However in none of the previous studies, the accuracy of acoustical flow estimation was investigated neither during sleep nor in people with obstructive sleep apnea (OSA). In this study, we recorded tracheal sound, flow rate and head position from 11 individuals with OSA during sleep and wakefulness. We investigated two approaches for calibrating the parameters of acoustical flow estimation model based on the known data recorded during wakefulness and sleep. The results show that the acoustical flow estimation parameters change from wakefulness to sleep. Therefore, if the model is calibrated based on the data recorded during wakefulness, although the estimated flow follows the relative variations of the recorded 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 flow recordings during sleep, the flow estimation error is less than 5%. These results confirm the reliability of acoustical methods for estimating breathing flow during sleep and detecting the partial or complete obstructions of the upper airways during sleep.

Sleep apnea is a common respiratory disorder during sleep, which is described as a cessation of airflow to the lungs that lasts at least for 10 s and is associated with at least 4% drop in blood's oxygen saturation level (S(a)O(2)). The current gold standard method for sleep apnea assessment is full-night polysomnography (PSG). However, its high cost, inconvenience for patients, and immobility have persuaded researchers to seek simple and portable devices to detect sleep apnea. In this article, we report on developing a new method for sleep apnea detection and monitoring, which only requires two data channels: tracheal breathing sounds and the pulse oximetry (S(a)O(2) signal). It includes an automated method that uses the energy of breathing sounds signals to segment the signals into sound and silent segments. Then, the sound segments are classified into breath, snore, and noise segments. The S(a)O(2) signal is analyzed automatically to find its rises and drops. Finally, a weighted average of different features extracted from breath segments, snore segments and S(a)O(2) signal are used to detect apnea and hypopnea events. The performance of the proposed approach was evaluated on the data of 66 patients recorded simultaneously with their full-night PSG study, and the results were compared with those of the PSG. The results show high correlation (0.96, P0.0001) between the outcomes of our system and those of the PSG. Also, the proposed method has been found to have sensitivity and specificity values of more than 91% in differentiating simple snorers from obstructive sleep apnea patients.

Spectral analysis is one of the most common methods in sound signal analysis for approximating sound power. However, since the sound power is usually presented in logarithmic scale, it is important to consider the non-linearity effects of logarithm function. In this study, the misconceptions and implementation issues regarding noise power reduction and average power calculation are described. Respiratory sound analysis is utilized as an example to show these issues in a practical application. The results indicate that most of the errors happen during noise power reduction; they can be either due to substituting noise reduction by sound detection concept or/and representing the noise power in the very low frequency components instead of the signal power. Also, if the average powers of the signals are calculated in the wrong scale, the results do not represent the acoustical characteristics of the sounds; this is shown by considering the flow-sound relationship at different flow rates.

Obstructive sleep apnea (OSA) is a common respiratory disorder during sleep, in which the airways are collapsed and impair the respiration. Apnea is s cessation of airflow to the lungs which lasts at least for 10s. The current gold standard method for OSA assessment is full night polysomnography (PSG); however, its high cost, inconvenience for patients and immobility have persuaded researchers to seek simple and portable devices to detect OSA. In this paper, we report on developing a new system for OSA detection and monitoring, which only requires two data channels: tracheal breathing sounds and the blood oxygen saturation level (S(a)O(2)). A fully automated method was developed that uses the energy of breathing sounds signals to segment the signals into sound and silent segments. Then, the sound segments are classified into breath, snore (if exists) and noise segments. The S(a)O(2) signal is analyzed to find the rises and drops in the S(a)O(2) signal. Finally, a fuzzy algorithm was developed to use this information and detect apnea and hypopnea events. The method was evaluated on the data of 40 patients simultaneously with full night PSG study, and the results were compared with those of the PSG. The results show high correlation (96%) between our system and PSG. Also, the method has been found to have sensitivity and specificity values of more than 90% in differentiating simple snorers from OSA patients.

In this paper a new model for swallowing sounds generation is proposed. The model consists of two systems: one that simulates the movements and activities of the muscles and the bones in the pharynx and the interactions between the bolus and the pharynx structure, followed by the second system representing the transfer function of the esophageal wall structure, tissue and skin beneath the microphone as the bolus travels through. Cepstrum analysis was used to estimate this transfer function. Based on the estimated transfer function, Symlet wavelet of order 8 was used to find the impulse train representing the output of the first part of the model. To have a general understanding whether such a model can be a representative of swallowing sound generation, its performance in describing differences in swallowing sounds characteristics for two bolus textures with different viscosities was evaluated. The results show the model may predict the changes in the swallowing sounds characteristics and the outcomes of the model comply with the common knowledge of the swallowing mechanism.

Recently, with the advances in digital signal processing, compression of biomedical signals has received great attention for telemedicine applications. In this paper, an adaptive transform coding-based method for compression of respiratory and swallowing sounds is proposed. Using special characteristics of respiratory sounds, the recorded signals are divided into stationary and nonstationary portions, and two different bit allocation methods (BAMs) are designed for each portion. The method was applied to the data of 12 subjects and its performance in terms of overall signal-to-noise ratio (SNR) values was calculated at different bit rates. The performance of different quantizers was also considered and the sensitivity of the quantizers to initial conditions has been alleviated. In addition, the fuzzy clustering method was examined for classifying the signal into different numbers of clusters and investigating the performance of the adaptive BAM with increasing the number of classes. Furthermore, the effects of assigning different numbers of bits for encoding stationary and nonstationary portions of the signal were studied. The adaptive BAM with variable number of bits was found to improve the SNR values of the fixed BAM by 5 dB. Last, the possibility of removing the training part for finding the parameters of adaptive BAMs for each individual was investigated. The results indicate that it is possible to use a predefined set of BAMs for all subjects and remove the training part completely. Moreover, the method is fast enough to be implemented for real-time application.

While lung sounds intensity is significantly different during inspiratory and expiratory phases, such difference is not audible between the two respiratory phases when listening to tracheal breath sounds. In this study we investigated whether any difference exists between the average power and log-variance of the band-pass filtered tracheal breath sound between the respiratory phases. We used data from 9 healthy subjects without any pulmonary diseases at 4 different flow rates (low, medium, high and very high) and compared the two features at six different frequency ranges from 70 to 1200Hz. The most pronounced differences between the two respiratory phases were found in the 300-450Hz and 800-1000Hz for the average power and log-variance, respectively.

Compression of biomedical signals is of great interest in telemedicine applications for a fast and reliable online data transfer. In this paper, an adaptive method for compression of respiratory and swallowing sounds is proposed. The method is based on the transform coding of the signal and adaptively assigns two different bit allocations for encoding the stationary and non-stationary parts of the signal. The method was applied to data of 12 subjects and its performance was compared with other methods of bit allocation such as constant bit allocation. The results show that the proposed adaptive bit allocation method improves the SNR of the signal by 3 dB compared to that of constant bit allocation method. Furthermore, the possibility of removing the requirement of updating the bit allocation method for each subject was investigated. The results confirm that with a few training data, the proposed method can be used in a fully automated mode without the need to adjust the adaptive bit allocation for every signal separately; hence, it is faster for online coding applications.

In recent years swallowing sounds analysis have received great attention for observing the abnormalities in swallowing mechanisms. In this paper a comprehensive set of features were extracted from time and frequency domains characteristics of the signals. 111 features were obtained from different parts of swallowing sounds including initial discrete sounds (IDS), bolus transmission sounds (BTS) and the entire swallowing sounds signal (WHL). Reducing the number of features and selecting a set of most important ones is a crucial step in sketching the signal characteristics, observing the signal variations in classification problems. Therefore, in this study features were examined thoroughly and arranged by maximizing the Mahalanobis distances between normal and dysphagic classes. The results indicate low- and high-frequency components represent the main characteristics of the signals for IDS segment of the swallowing sound, while the medium frequency components play the principal role for BTS segment. Different feature subsets with variable number of features were investigated for classifying normal and dysphagic swallowing sound signals. It was found that the overall performances of the feature subset extracted from WHL was superior to the results of the feature subsets extracted from IDS or BTS individually.

For any respiratory sound analysis or assessment, respiratory flow must also be measured simultaneously with the sounds. However, due to difficulties and/or inaccuracy of the most flow measurement techniques, several researchers have attempted to estimate flow from respiratory sounds. However, all of the proposed methods heavily depend on the availability of different rates of flow for calibration of the model, which makes their use limited by a large degree. In this paper, a robust and novel method for estimating flow using entropy of the band pass filtered tracheal sounds is proposed. The proposed method is independent of the flow rate chosen for calibration; it requires only one breath for calibration and can estimate any flow rate even out of the range of calibration flow. The method was tested on data of 10 healthy subjects at three different flow rates above 15 ml/s/kg. The estimation error was found to be 7.3 plusmn 2.0% and 7.4 plusmn 3.2% for inspiration and expiration phases, respectively

Relationship between respiratory sounds and flow has always been of interest for researchers and physicians. However, the flow-sound relationship at very low flow rate has been questionable because breath sounds must exceed a minimum flow in order to be audible and different from the background noise. This study aimed to find the minimum critical flow rates for respiratory sounds to be audible and different from background noise. Tracheal and lung sound signals of healthy subjects in two groups of adults (12 subjects) and children (9 subjects) were studied. The values of minimum critical flow were determined comparing the spectrogram of the respiratory sounds at very low flow with that of during breath hold. The values of minimum critical flow for trachea! sounds were found to be 3.7 plusmn 1.7 ml/s/kg and 2.7 plusmn1.7 ml/s/kg for adults and children, respectively. The minimum critical flows for lung sounds were found as 6.2 plusmn 1.8 ml/s/kg and 4.0 plusmn 2.9 ml/s/kg for adults and children, respectively. I

Heart sounds are the main unavoidable interference in lung sound recording and analysis. Hence, several techniques have been developed to reduce or cancel heart sounds (HS) from lung sound records. The first step in most HS cancellation techniques is to detect the segments including HS. This paper proposes a novel method for HS localization using entropy of the lung sounds. We investigated both Shannon and Renyi entropies and the results of the method using Shannon entropy were superior. Another HS localization method based on multiresolution product of lung sounds wavelet coefficients adopted from was also implemented for comparison. The methods were tested on data from 6 healthy subjects recorded at low (7.5 ml/s/kg) and medium (15 ml/s/kg) flow rates. The error of entropy-based method using Shannon entropy was found to be 0.1 /spl plusmn/ 0.4% and 1.0 /spl plusmn/ 0.7% at low and medium flow rates, respectively, which is significantly lower than that of multiresolution product method and those of other methods reported in previous studies. The proposed method is fully automated and detects HS included segments in a completely unsupervised manner.

The human being in the environments of modern technology has to endure stresses of many and varied kinds of vibrations [1]. Measuring vibration is an important tool in rehabilitation and biomechanical fields of research. We have proposed image processing as a new method to record and determine the frequency response of human body. The subjects were exposed to whole body periodical vibration while standing on a shaking table. Two digital camcorders were used to capture the motion of colored pencil-dot markers on the skin of human body (forehead) and on the edge of the shaking table. After color spotting each frame, the binary image results were processed using new circle factor criteria proposed in this work, for fast finding circles based on second order statistics. The extracted points were calibrated using our own extended version of the direct linear transformation (DLT) method. We subsequently used Borland Delphi 5.0 language to develop useful software for measuring and analyzing human body vibration. As a result, it was clear that the proposed method was lower noise-sensitive in comparison to accelerometer. In order to investigate the validity of the software, the obtained mechanical impedance of the body were compared with other investigations in literature and showed to be compatible. The main advantage of this method is working with a simple user-familiar hardware with no external device attached to the subject and also a user-friendly-software.Copyright © 2004 by ASME

In recent years, respiratory flow estimation using tracheal sounds has received considerable attention. In this paper, four different features of tracheal sound are investigated and their relationships with flow at different target flow rates are examined during inspiration and expiration phases. The features include average power (AvgPwr), logarithm of the variance (LogV ar), logarithm of the range (LogRng) and logarithm of the envelop (LogEnv) of tracheal sound. For each feature a linear model is fitted to the flow and the feature. The results show that LogV ar is the best feature to describe flow-sound relationship with a linear model, while the slope of the linear model using AvgPwr shows the largest deviation from a line with changes in target flow rates. Also, the distance from origin of the linear model using any feature changes linearly with variations of target flow.