A study to identify obstructive sleep apnea syndrome based on 24 h ambulatory blood pressure data.
10.7507/1001-5515.202111030
- Author:
Jian ZHANG
1
;
Jiaojie REN
2
;
Shuchen SUN
3
;
Zhengbo ZHANG
4
Author Information
1. Medical School of Chinese PLA, Beijing 100853, P. R. China.
2. Department of Traditional Chinese Medicine, The First Affiliated Hospital of Air Force Military Medical University, Chinese PLA, Xi'an 710032, P. R. China.
3. Department of Otolaryngology, Guang'anmen Hospital, Chinese Academy of Traditional Chinese Medicine, Beijing 100000, P. R. China.
4. Center for Artificial Intelligence in Medicine, Chinese PLA General Hospital, Beijing 100853, P. R. China.
- Publication Type:Journal Article
- Keywords:
Ambulatory blood pressure;
Disease screening;
Feature extraction;
Machine learning;
Obstructive sleep apnea hypopnea
- MeSH:
Blood Pressure;
Blood Pressure Monitoring, Ambulatory;
Humans;
Hypertension/complications*;
Polysomnography;
Sleep Apnea, Obstructive/diagnosis*
- From:
Journal of Biomedical Engineering
2022;39(1):1-9
- CountryChina
- Language:Chinese
-
Abstract:
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.
