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目的:探讨夜间脉搏血氧饱和度(SpOn 2)监测对阻塞性睡眠呼吸暂停低通气综合征(OSAHS)预测和分类的价值。n 方法:回顾性分析2018年1月至2019年12月就诊于天津医科大学总医院睡眠中心的580例打鼾患者的临床资料,男418例,女162例,年龄13~85(49±14)岁,所有患者均接受了整夜多导睡眠监测(PSG),睡眠呼吸暂停低通气指数(AHI)为0~101.4(43.06±27.47)次/h。其中,非OSAHS组(AHI<5次/h)52例,轻度OSAHS组(5次/h30次/h)361例。从SpOn 2信号中提取13个指标,与AHI做相关性分析后,最终筛选11个与AHI相关的SpOn 2指标(3%氧减饱和度回升指数,SpOn 2低于90%曲线下面积,最低SpOn 2平均值,最低SpOn 2,平均SpOn 2,SpOn 2分别低于95%、90%、85%、80%、75%、70%的时间百分比),加入3个人口学指标[性别、年龄、体质量指数(BMI)]作为全部特征。分别利用多元线性回归(MLR)方法和反向传播神经网络(BPNN)多分类方法,进行AHI预测和OSAHS严重程度分类。采用SPSS 25.0软件进行统计学分析,计量资料均采用Pearson相关检验。n 结果:对MLR方法和BPNN多分类方法进行评价。MLR方法获得了较高预测性能,其模型拟合优度n r2=0.848(n P<0.05),预测相关系数n r=0.901(n P<0.05)。BPNN多分类方法分类结果的特异度和阴性预测率均在90%左右,敏感度和阳性预测率也较高,其中非OSAHS组分类敏感度为88.46%±4.50%,重度OSAHS组分类的敏感度为94.74%±0.76%。n 结论:基于夜间SpOn 2监测仪记录的信号,利用MLR模型进行AHI预测以及利用BPNN模型进行多分类的方法,可能对OSAHS有较高的预测和分类价值。n “,”Objective:To explore the value of night pulse oximetry monitoring in the prediction and classification of obstructive sleep apnea hypopnea syndrome (OSAHS).Methods:From January 2018 to December 2019, 580 snoring patients admitted to the Sleep Center of Tianjin Medical University General Hospital were analyzed retrospectively. There were 418 males and 162 females, aging 13-85(49±14) years. All subjects underwent polysomnography, and the apnea hypopnea index (AHI)was 0-101.4(43.06±27.47) times/hour. There were 52 cases in the non-OSAHS group (AHI<5 times/h), 69 cases in the mild OSAHS group (5 times/h30 times/h) was 94.74%±0.76%.n Conclusion:Based on the signals recorded by the SpOn 2 monitor, the methods of using MLR model for AHI prediction and using BPNN model for multi-classification may have higher value for the prediction and classification of OSAHS.n