Variations in respiratory sounds in relation to fluid accumulation in the upper airways.

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 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%.

Acoustical flow estimation in patients with obstructive sleep apnea during sleep.

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.

Assessment of obstructive sleep apnea and its severity during wakefulness.

In this article, a novel technique for assessment of obstructive sleep apnea (OSA) during wakefulness is proposed; the technique is based on tracheal breath sound analysis of normal breathing in upright sitting and supine body positions. We recorded tracheal breath sounds of 17 non-apneic individuals and 35 people with various degrees of severity of OSA in supine and upright sitting positions during both nose and mouth breathing at medium flow rate. We calculated the power spectrum, Kurtosis, and Katz fractal dimensions of the recorded signals and used the one-way analysis of variance to select the features, which were statistically significant between the groups. Then, the maximum relevancy minimum redundancy method was used to reduce the number of characteristic features to two. Using the best two selected features, we classified the participant into severe OSA and non-OSA groups as well as non-OSA or mild vs. moderate and severe OSA groups; the results showed more than 91 and 83% accuracy; 85 and 81% specificity; 92 and 95% sensitivity, for the two types of classification, respectively. The results are encouraging for identifying people with OSA and also prediction of OSA severity. Once verified on a larger population, the proposed method offers a simple and non-invasive screening tool for prediction of OSA during wakefulness.

Obstructive sleep apnea prediction during wakefulness.

In this paper, a novel technique based on signal processing of breath sounds during wakefulness for prediction of obstructive sleep apnea (OSA) is proposed. We recorded tracheal breath sounds of 35 people with various severity of OSA and 17 non-apneic individuals; the breath sounds were recorded in supine and upright positions during both nose and mouth breathing at medium flow rate. Power spectrum, Kurtosis and Katz fractal dimension of the recorded signals in every posture and breathing maneuver were calculated. We used one-way ANOVA to select the features with most significant differences between the groups followed by the Maximum Relevancy Minimum Redundancy (mRMR) method to reduce the number of characteristic features to three, and investigated the separability of the groups based on the three selected features. The results are encouraging for classification of patients using the selected features. Once being verified on a larger population, the proposed method offers a fast, simple and non-invasive screening tool for prediction of OSA during wakefulness.

Acoustical screening for obstructive sleep apnea during wakefulness.

In this paper a new non-invasive method for screening patients with obstructive sleep apnea (OSA) during wakefulness is proposed. Eight people with OSA and eight non-apneic individuals participated in this study. The tracheal breath sound was recorded in supine and upright positions during both nose and mouth breathing maneuvers. Spectral analysis of the respiratory sound signals showed the variation in the average power of the sounds at different positions to be a characteristic feature discriminating the two groups. Using this feature, the OSA and non-apneic participants were classified by quadratic discriminant analysis (QDA). The specificity, sensitivity, and classification accuracy of the classifier were found to be 100%, 87.5%, and 95.75%, respectively. These results are encouraging for the use of the proposed method as a fast, simple and screening tool for diagnosis of OSA during wakefulness.