Abstract：
Background and Objective. Sleep-disordered breathing (SDB) poses health risks linked to hypertension, cardiovascular disease, and diabetes. However, the time-consuming and costly standard diagnostic method, polysomnography (PSG), limits its wide adoption and leads to underdiagnosis. To tackle this, cost-effective algorithms using single-lead signals (like respiratory, blood oxygen, and electrocardiogram) have emerged. Despite respiratory signals being preferred for SDB assessment, a lack of comprehensive reviews addressing their algorithmic scope and performance persists. This paper systematically reviews 2012-2022 literature, covering signal sources, processing, feature extraction, classification, and application, aiming to bridge this gap and provide future research references.Methods. This systematic review followed the registered PROSPERO protocol (CRD42022385130), initially screening 342 papers, with 32 studies meeting data extraction criteria.Results. Respiratory signal sources include nasal airflow (NAF), oronasal airflow (OAF), and respiratory movement-related signals such as thoracic respiratory effort (TRE) and abdominal respiratory effort (ARE). Classification techniques include threshold rule-based methods (8), machine learning models (13), and deep learning models (11). The NAF-based algorithm achieved the highest average accuracy at 94.11%, surpassing 78.19% for other signals. Hypopnea detection sensitivity with single-source respiratory signals remained modest, peaking at 73.34%. The TRE and ARE signals proved to be reliable in identifying different types of SDB because distinct respiratory disorders exhibited different patterns of chest and abdominal motion.Conclusions. Multiple detection algorithms have been widely applied for SDB detection, and their accuracy is closely related to factors such as signal source, signal processing, feature selection, and model selection.
摘要：
目标：
睡眠呼吸紊乱（SDB）会带来与高血压相关的健康风险，心血管疾病,和糖尿病。然而,耗时且昂贵的标准诊断方法，多导睡眠图（PSG），限制了其广泛采用并导致诊断不足。为了解决这个问题，使用单导联信号(如呼吸，血氧,和心电图）已经出现。尽管呼吸信号是SDB评估的首选信号，缺乏针对其算法范围和性能的全面审查。本文系统回顾了2012-2022年的文献，覆盖信号源，processing,特征提取,分类，和应用，旨在弥补这一差距,为今后的研究提供参考。＆＃xD;方法：＆＃xD;这项系统审查遵循注册的PROSPERO协议（CRDXXXXXXX），最初筛选342篇论文，32项研究符合数据提取标准。＆＃xD;结果：＆＃xD;呼吸信号源包括鼻气流（NAF），口鼻气流（OAF），和呼吸运动相关的信号，如胸部呼吸努力（TRE）和腹部呼吸努力（ARE）。分类技术包括基于阈值规则的方法(8)，机器学习(ML)模型(13)，和深度学习(DL)模型(11)。基于NAF的算法获得了最高的平均准确率，为94.11%，其他信号超过78.19%。单源呼吸信号的低通气检测灵敏度仍然适中，峰值为73.34%。TRE和ARE信号被证明在识别不同类型的SDB方面是可靠的，因为不同的呼吸系统疾病表现出不同的胸部和腹部运动模式。＆＃xD;结论：＆＃xD;多种检测算法已广泛应用于SDB检测，它们的准确性与信号源等因素密切相关，信号处理，特征选择,和模型选择。
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