Using Tracheal Breathing Sounds and Anthropometric Information for Screening Obstructive Sleep Apnoea During Wakefulness

Undiagnosed OSA significantly increases perioperative morbidity and mortality in patients undergoing surgery under general anesthesia. Tracheal breathing sound characteristics during wakefulness have shown a high correlation with the apnoea-hypopnea index (AHI), while they are also affected by anthropometric parameters, including sex, age, etc. In this study breathing sounds of 122 individuals (71 with AHI <15 as non-OSA and 51 with AHI > 15 as OSA) were recorded during wakefulness in supine position. The spectra and bi-spectra of 81 (47 non-OSA) individuals’ signals, which were randomly selected, were analyzed to extract the most significant features with the lowest sensitivity to the anthropometric parameters. Using a support vector machine (SVM) classifier, these features resulted in 72.1, 64.7 and 77.5% testing classification accuracy, sensitivity and specificity, respectively. Furthermore, the auhtors investigated classifying subjects into subgroups related to each anthropometric parameter and incorporating a voting procedure. This resulted in 83.6, 74.5 and 90.1% testing classification accuracy, sensitivity and specificity, respectively. The study indicated that it may possible to positively utilise the anthropometric information to enhance the classification accuracy for a reliable OSA screening procedure during wakefulness.

https://www.ncbi.nlm.nih.gov/pubmed/31210085