Removing of Snoring Segments from Tracheal Breathing Sounds using a Wavelet-based Algorithm

Tracheal sounds represent information about the upper airway and respiratory airflow, however, they can be contaminated by the snoring sounds. the sound of snoring has spectral content in a wide range that overlaps with that of breathing sounds during sleep. for assessing respiratory airflow using tracheal breathing sound, it is essential to remove the effect of snoring. in this paper, an automatic and unsupervised wavelet-based snoring removal algorithm is presented. simultaneously with full-night polysomnography, the tracheal sound signals of 9 subjects with different levels of airway obstruction were recorded by a microphone placed over the trachea during sleep. the segments of tracheal sounds that were contaminated by snoring were manually identified through listening to the recordings. the selected segments were automatically categorized based on including discrete or continuous snoring pattern. segments with discrete snoring were analyzed by an iterative wave-based filtering optimized to separate large spectral components related to snoring from smaller ones corresponded to breathing. those with continuous snoring were first segmented into shorter segments. then, each short segments were similarly analyzed along with a segment of normal breathing extracted from the recordings during wakefulness. the algorithm was evaluated by visual inspection of the denoised sound energy and comparison of the spectral densities before and after removing snores, where the overall rate of detectability of snoring was less than 2%.clinical relevance- the algorithm provides a way of separating snoring pattern from the tracheal breathing sounds. therefore, each of them can be analyzed separately to assess respiratory airflow and the pathophysiology of the upper airway during sleep