基于24小时动态血压数据识别阻塞性睡眠呼吸暂停综合征研究  

A study to identify obstructive sleep apnea syndrome based on 24 h ambulatory blood pressure data

作　　者：张健[1] 任皎洁 孙书臣[3] 张政波[4] ZHANG Jian;REN Jiaojie;SUN Shuchen;ZHANG Zhengbo(Medical School of Chinese PLA,Beijing 100853,P.R.China;Department of Traditional Chinese Medicine,The First Affiliated Hospital of Air Force Military Medical University,Chinese PLA,Xi'an 710032,P.R.China;Department of Otolaryngology,Guang’anmen Hospital,Chinese Academy of Traditional Chinese Medicine,Beijing 100000,P.R.China;Center for Artificial Intelligence in Medicine,Chinese PLA General Hospital,Beijing 100853,P.R.China)

机构地区：[1]中国人民解放军医学院,北京100853 [2]中国人民解放军空军军医大学第一附属医院中医科,西安710032 [3]中国中医科学院广安门医院耳鼻喉科,北京100000 [4]中国人民解放军总医院医学人工智能研究中心,北京100853

出　　处：《生物医学工程学杂志》2022年第1期1-9,共9页Journal of Biomedical Engineering

基　　金：国家自然科学基金面上项目(62171471);北京市科委医药协同科技创新研究(Z181100001918023);解放军总医院大数据研发项目(2018MBD-009)。

摘　　要：睡眠呼吸暂停会导致患者心跳呼吸骤停、睡眠节律紊乱、夜间低血氧和血压异常波动,最终导致高血压患者夜间靶器官损害。阻塞性睡眠呼吸暂停低通气综合征(OSAHS)发病率极高,严重影响了患者的身心健康。本研究尝试从24小时动态血压数据中提取与OSAHS相关的特征,通过机器学习模型识别OSAHS,从而实现该疾病的鉴别诊断。研究数据来自2018年12月至2019年12月解放军总医院睡眠门诊收集的339例患者的动态血压检查数据,其中经多导睡眠监测(PSG)确诊的OSAHS患者115例,非OSAHS患者224例。根据OSAHS患者血压临床变化特点,定义了特征提取规则并开发算法提取特征,而后使用logistic回归、lightGBM等模型对疾病进行分类预测。结果表明本研究所训练的lightGBM模型的识别准确率为80.0%,精确率为82.9%,召回率为72.5%,受试者工作特征曲线下面积(AUC)为0.906,所定义的动态血压特征能够有效用于OSAHS检测。本研究为OSAHS筛查提供了一种新的思路和方法。Sleep apnea causes cardiac arrest,sleep rhythm disorders,nocturnal hypoxia and abnormal blood pressure fluctuations in patients,which eventually lead to nocturnal target organ damage in hypertensive patients.The incidence of obstructive sleep apnea hypopnea syndrome(OSAHS)is extremely high,which seriously affects the physical and mental health of patients.This study attempts to extract features associated with OSAHS from 24-hour ambulatory blood pressure data and identify OSAHS by machine learning models for the differential diagnosis of this disease.The study data were obtained from ambulatory blood pressure examination data of 339 patients collected in outpatient clinics of the Chinese PLA General Hospital from December 2018 to December 2019,including 115 patients with OSAHS diagnosed by polysomnography(PSG)and 224 patients with non-OSAHS.Based on the characteristics of clinical changes of blood pressure in OSAHS patients,feature extraction rules were defined and algorithms were developed to extract features,while logistic regression and lightGBM models were then used to classify and predict the disease.The results showed that the identification accuracy of the lightGBM model trained in this study was 80.0%,precision was 82.9%,recall was 72.5%,and the area under the working characteristic curve(AUC)of the subjects was 0.906.The defined ambulatory blood pressure features could be effectively used for identifying OSAHS.This study provides a new idea and method for OSAHS screening.

关 键 词：动态血压 阻塞性睡眠呼吸暂停 特征提取 机器学习 疾病筛查 

分 类 号：R766[医药卫生—耳鼻咽喉科]