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.