  Obstructive Sleep Apnea-Hypopnea Syndrome (OSAHS) is a chronic breathing
disorder caused by a blockage in the upper airways. Snoring is a prominent
symptom of OSAHS, and previous studies have attempted to identify the
obstruction site of the upper airways by snoring sounds. Despite some progress,
the classification of the obstruction site remains challenging in real-world
clinical settings due to the influence of sleep body position on upper airways.
To address this challenge, this paper proposes a snore-based sleep body
position recognition dataset (SSBPR) consisting of 7570 snoring recordings,
which comprises six distinct labels for sleep body position: supine, supine but
left lateral head, supine but right lateral head, left-side lying, right-side
lying and prone. Experimental results show that snoring sounds exhibit certain
acoustic features that enable their effective utilization for identifying body
posture during sleep in real-world scenarios.
