Li Chenyang, Ye Jilun, Guan Jian
Department of Otolaryngology-Head and Neck Surgery & Center of Sleep Medicine, Shanghai Jiao Tong University School of Medicine Affiliated Sixth People's Hospital, Shanghai, 200233.
Shanghai Key Laboratory of Sleep Disordered Breathing, Shanghai, 200233.
Zhongguo Yi Liao Qi Xie Za Zhi. 2024 Jul 30;48(4):373-379. doi: 10.12455/j.issn.1671-7104.240036.
Sleep disordered breathing (SDB) is a common sleep disorder with an increasing prevalence. The current gold standard for diagnosing SDB is polysomnography (PSG), but existing PSG techniques have some limitations, such as long manual interpretation times, a lack of data quality control, and insufficient monitoring of gas metabolism and hemodynamics. Therefore, there is an urgent need in China's sleep clinical applications to develop a new intelligent PSG system with data quality control, gas metabolism assessment, and hemodynamic monitoring capabilities. The new system, in terms of hardware, detects traditional parameters like nasal airflow, blood oxygen levels, electrocardiography (ECG), electroencephalography (EEG), electromyography (EMG), electrooculogram (EOG), and includes additional modules for gas metabolism assessment end-tidal CO and O concentration, and hemodynamic function assessment through impedance cardiography. On the software side, deep learning methods are being employed to develop intelligent data quality control and diagnostic techniques. The goal is to provide detailed sleep quality assessments that effectively assist doctors in evaluating the sleep quality of SDB patients.
睡眠呼吸障碍（SDB）是一种常见且患病率不断上升的睡眠障碍。目前诊断SDB的金标准是多导睡眠图（PSG），但现有的PSG技术存在一些局限性，如人工解读时间长、缺乏数据质量控制以及对气体代谢和血流动力学的监测不足。因此，在中国的睡眠临床应用中，迫切需要开发一种具有数据质量控制、气体代谢评估和血流动力学监测能力的新型智能PSG系统。新系统在硬件方面，可检测诸如鼻气流、血氧水平、心电图（ECG）、脑电图（EEG）、肌电图（EMG）、眼电图（EOG）等传统参数，还包括用于气体代谢评估的额外模块——呼气末二氧化碳和氧气浓度，以及通过阻抗心动图进行血流动力学功能评估。在软件方面，正在采用深度学习方法来开发智能数据质量控制和诊断技术。目标是提供详细的睡眠质量评估，有效协助医生评估SDB患者的睡眠质量。
