A Snoring Sound Dataset for Body Position Recognition: Collection, Annotation, and Analysis

TL;DR

This paper introduces a comprehensive snoring sound dataset with annotated sleep positions to improve body position recognition during sleep, addressing challenges posed by real-world clinical variability.

Contribution

It provides a large, labeled dataset of snoring sounds for sleep position recognition, facilitating research in sleep disorder diagnostics and monitoring.

Findings

Snoring sounds contain acoustic features indicative of sleep body position.

The dataset enables effective classification of sleep positions in real-world settings.

Experimental results demonstrate the dataset's utility for sleep position recognition.

Abstract

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.