SimuSOE：用于清醒状态下阻塞性睡眠呼吸暂停低通气综合征评估的模拟打鼾数据集
SimuSOE: A Simulated Snoring Dataset for Obstructive Sleep Apnea-Hypopnea Syndrome Evaluation during Wakefulness
AI总结：
针对OSAHS评估数据集构建难的问题，提出含省时采集方法的模拟打鼾数据集SimuSOE，用清醒时的模拟鼾声替代自然鼾声，验证了其在OSAHS初筛中的有效性。
摘要：
阻塞性睡眠呼吸暂停低通气综合征（OSAHS）是一种由上气道阻塞引发的常见慢性呼吸疾病。既往研究通过基于机器学习的系统推进OSAHS评估，这类系统在睡眠打鼾或语音信号数据集上训练。然而，构建用于训练精准快速的OSAHS评估系统的数据集存在挑战，原因包括：1）收集睡眠鼾声耗时；2）语音信号在反映上气道阻塞方面存在局限。本文提出一种用于OSAHS评估的新型打鼾数据集，命名为SimuSOE，其中引入了一种新颖且省时的鼾声采集方法以解决上述问题。具体而言，我们采用模拟打鼾（即患者主动发出的一类鼾声）来替代自然鼾声。实验结果表明，清醒状态下的模拟打鼾信号可作为OSAHS初步筛查的有效特征。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
