一种智能多参数睡眠诊断与分析系统的研制  

Development of an Intelligent Multi-Parameter Sleep Diagnosis and Analysis System

作　　者：李晨洋 叶继伦 关建[1,2] 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;School of Biomedical Engineering,Health Science Center,Shenzhen University,Shenzhen,518037)

机构地区：[1]上海交通大学医学院附属第六人民医院耳鼻咽喉头颈外科,上海市200233 [2]上海市睡眠呼吸障碍疾病重点实验室,上海市200233 [3]深圳大学医学部生物医学工程学院,深圳市518037

出　　处：《中国医疗器械杂志》2024年第4期373-379,共7页Chinese Journal of Medical Instrumentation

基　　金：国家自然科学基金资助项目(82271153);上海交通大学医工(理)交叉基金(YG2023LC11);深圳市科创委2023年重点项目(KJZD20230923114306013);上海市第六人民医院管理专项(Lyg1202309)。

摘　　要：睡眠呼吸障碍疾病(sleep disordered breathing,SDB)是常见的睡眠疾病,其患病率逐年上升。目前SDB诊断的金标准为多导睡眠监测(polysomnography,PSG),但现有的PSG监测技术存在人工判读时间长、缺乏数据质控、缺少气体代谢及血流动力学监测等问题。因此,研制具有数据质控、气体代谢及血流动力学监测功能的新型智能PSG是我国睡眠临床应用中迫切需要解决的问题。硬件方面,新系统检测鼻气流、血氧、心电、脑电、肌电、眼电等传统参数,且新增评估气体代谢功能的呼气末CO_(2)、O_(2)浓度及评估血流动力学功能的心阻抗检测模块。软件方面,基于深度学习方法研究智能数据质控、智能疾病诊断技术。目标是输出详细的睡眠质量评价报告,以有效地辅助医生充分评估SDB患者的睡眠质量。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 via end-tidal CO_(2) and O_(2) 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.

关 键 词：睡眠监测 气体代谢 血流动力学 数据质控 疾病诊断 

分 类 号：TH772[机械工程—仪器科学与技术]