SimuSOE: A Simulated Snoring Dataset for Obstructive Sleep Apnea-Hypopnea Syndrome Evaluation during Wakefulness

TL;DR

This paper introduces SimuSOE, a novel simulated snoring dataset collected during wakefulness, to improve OSAHS evaluation efficiency and effectiveness using machine learning.

Contribution

It presents a new, time-efficient method for collecting simulated snoring data to enhance OSAHS screening accuracy during wakefulness.

Findings

Simulated snoring signals are effective features for OSAHS screening.

The dataset facilitates rapid and accurate OSAHS evaluation.

SimuSOE enables better machine learning model training for sleep disorder detection.

Abstract

Obstructive Sleep Apnea-Hypopnea Syndrome (OSAHS) is a prevalent chronic breathing disorder caused by upper airway obstruction. Previous studies advanced OSAHS evaluation through machine learning-based systems trained on sleep snoring or speech signal datasets. However, constructing datasets for training a precise and rapid OSAHS evaluation system poses a challenge, since 1) it is time-consuming to collect sleep snores and 2) the speech signal is limited in reflecting upper airway obstruction. In this paper, we propose a new snoring dataset for OSAHS evaluation, named SimuSOE, in which a novel and time-effective snoring collection method is introduced for tackling the above problems. In particular, we adopt simulated snoring which is a type of snore intentionally emitted by patients to replace natural snoring. Experimental results indicate that the simulated snoring signal during wakefulness can serve as an effective feature in OSAHS preliminary screening.