A study to identify obstructive sleep apnea syndrome based on 24 h ambulatory blood pressure data_Journal of Biomedical Engineering

Authors：

ZHANG Jian ¹ , REN Jiaojie ² ,  SUN Shuchen ³ ,  ZHANG Zhengbo ⁴

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;

Corresponding author：

SUN Shuchen, Email: sunsc0922@163.com; ZHANG Zhengbo, Email: zhengbozhang@126.com

Keywords：

Ambulatory blood pressure; Obstructive sleep apnea hypopnea; Feature extraction; Machine learning; Disease screening

DOI：

10.7507/1001-5515.202111030

Video：

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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.

Citation： ZHANG Jian, REN Jiaojie, SUN Shuchen, ZHANG Zhengbo. A study to identify obstructive sleep apnea syndrome based on 24 h ambulatory blood pressure data. Journal of Biomedical Engineering, 2022, 39(1): 1-9. doi: 10.7507/1001-5515.202111030 Copy

1.	Noda A, Okada T, Hayashi H, et al. 24-hour ambulatory blood pressure variability in obstructive sleep apnea syndrome. Chest, 1993, 103(5): 1343-1347.
2.	Neves B, Raimundo A, Lutas A, et al. Ambulatory blood pressure monitoring utility in obstructive sleep apnea diagnosis. J Am Soc Hypertens, 2015, 9(4 Suppl): e45.
3.	Stojanovic M, Vujovic S, Gajic M T, et al. Ambulatory blood pressure monitoring as a screening method for obstructive sleep apnea syndrome. J Hypertens, 2015, 33: e473.
4.	Li N, Zhang P, Ding H, et al. A4951 The characteristics of ambulatory blood pressure in Chinese hypertensive patients with obstructive sleep apnea. J Hypertens, 2018, 36: e107-e108.
5.	潘悦达, 王东博, 韩德民. 我国成人阻塞性睡眠呼吸暂停低通气综合征患病率的Meta分析. 医学信息, 2019, 32(7): 73-77, 81.
6.	Cuspidi C, Tadic M, Sala C, et al. Blood pressure non-dipping and obstructive sleep apnea syndrome: a meta-analysis. J Clin Med, 2019, 8(9): 1367.
7.	赵建强, 来春林, 刘晓红, 等. 老年高血压病人动态血压曲线下面积与颈动脉硬化关系的探讨. 中西医结合心脑血管病杂志, 2008(7): 767-769.
8.	崔剑, 赵子彦, 赵子婴. 拟合动态血压波动曲线并计算曲线下面积的数学方法. 数理医药学杂志, 2005(1): 5-7.
9.	Pinnaduwage D, Beyene J, Fallah S. Genome-wide linkage analysis of systolic blood pressure slope using the genetic analysis workshop 13 data sets. BioMed Central, 2003, 4(1): 1-6.
10.	Liu H, Lu R, Shastri S, et al. Assessing extracellular volume in hemodialysis patients using intradialytic blood pressure slopes. Nephron, 2018, 139(2): 120-130.
11.	Kelley K, Light R P, Agarwal R. Trended cosinor change model for analyzing hemodynamic rhythm patterns in hemodialysis patients. Hypertension, 2007, 50(1): 143-150.
12.	van Rijn-Bikker P C, Snelder N, Ackaert O, et al. Nonlinear mixed effects modeling of the diurnal blood pressure profile in a multiracial population. Am J Hypertens, 2013, 26(9): 1103-1113.
13.	Madden J M, Browne L D, Li X, et al. Morning surge in blood pressure using a random effects multiple component cosinor model. Stat Med, 2018, 37(10): 1682-1695.
14.	Torres G, Sánchez-de-la-torre M, Martínez-alonso M, et al. Use of ambulatory blood pressure monitoring for the screening of obstructive sleep apnea. J Clin Hypentens, 2015, 17(10): 802-809.
15.	Chuiko G P, Dvornik O V, Yaremchuk O, et al. Ambulatory blood pressure monitoring: modeling and data mining. ICTES, 2019: 85-95.
16.	王琳, 韩敏, 王保为, 等. 非高血压OSA患者动态血压相关性研究. 临床耳鼻咽喉头颈外科杂志, 2019, 33(7): 666-669.
17.	Caffo B, Diener-West M, Punjabi N M, et al. A novel approach to prediction of mild obstructive sleep disordered breathing in a population-based sample: the sleep heart health study. Sleep, 2010, 33(12): 1641-1648.
18.	陈欢意. 阻塞性睡眠呼吸暂停综合征合并高血压患者的动态血压状况分析. 中国医药科学, 2016, 6(12): 175-178, 209.
19.	黄泰. 高血压患者阻塞性睡眠呼吸暂停综合征筛查评分的建立与验证. 合肥: 安徽医科大学, 2020.
20.	Ghulam E M. Modeling unbalanced nested repeated measures data in the presence of informative drop-out with application to ambulatory blood pressure monitoring data. Cincinnati: University of Cincinnati, 2019.
21.	吴巧珍, 陆燕芳, 王伟, 等. 同型半胱氨酸和血压波动对高血压合并阻塞性睡眠呼吸暂停低通气综合征患者的预测价值. 中华医学杂志, 2020, 100(28): 2192-2197.
22.	罗新安, 孙正良. 阻塞性睡眠呼吸暂停低通气综合征的动态血压特征. 山东大学耳鼻喉眼学报, 2006(4): 343-344, 348.
23.	Lyshova O V, Kostenko I I. Predictors of obstructive sleep apnea syndrome according to results of clinical examination and ambulatory blood pressure monitoring. Kardiologiya, 2018, 58(9): 12-20.
24.	孙婉璐. 逐波观察阻塞性睡眠呼吸暂停患者睡眠中动态血压变化. 北京: 首都医科大学, 2016.
25.	马春梅, 刘惠亮, 韩玮, 等. 高血压合并阻塞性睡眠呼吸暂停综合征的动态血压及血压节律观察. 武警医学院学报, 2008, 68(5): 402-404.
26.	王慧珍, 陆虹. 高血压合并阻塞性睡眠呼吸暂停的动态血压特点. 天津医药, 2005(12): 775-776.
27.	Chung F, Elsaid H. Screening for obstructive sleep apnea before surgery: why is it important?. Curr Opin Anaesthesiol, 2009, 22(3): 405-411.
28.	苗晓东, 潘蕊, 朱东放, 等. 动态心电结合动态血压在筛查睡眠呼吸暂停综合征中的应用. 黑龙江医药, 2015, 28(5): 1143-1145.
29.	Furlan S F, Genta-Pereira D C, Omote D D Q, et al. Accuracy of non-dipping blood pressure in predicting obstructive sleep apnea in patients submitted to ambulatory blood pressure monitoring. Am J Respir Crit Care Med, 2017, 195: A2608.
30.	于佳岚, 高国伟. 动态血压监测过程对老年高血压患者睡眠质量及血压监测结果的影响研究. 医学综述, 2013, 19(22): 4205-4208.
31.	李吉. 24h动态血压对高血压患者睡眠质量及血压监测结果的影响. 中外医学研究, 2016, 14(16): 148-149.

1. Noda A, Okada T, Hayashi H, et al. 24-hour ambulatory blood pressure variability in obstructive sleep apnea syndrome. Chest, 1993, 103(5): 1343-1347.
2. Neves B, Raimundo A, Lutas A, et al. Ambulatory blood pressure monitoring utility in obstructive sleep apnea diagnosis. J Am Soc Hypertens, 2015, 9(4 Suppl): e45.
3. Stojanovic M, Vujovic S, Gajic M T, et al. Ambulatory blood pressure monitoring as a screening method for obstructive sleep apnea syndrome. J Hypertens, 2015, 33: e473.
4. Li N, Zhang P, Ding H, et al. A4951 The characteristics of ambulatory blood pressure in Chinese hypertensive patients with obstructive sleep apnea. J Hypertens, 2018, 36: e107-e108.
5. 潘悦达, 王东博, 韩德民. 我国成人阻塞性睡眠呼吸暂停低通气综合征患病率的Meta分析. 医学信息, 2019, 32(7): 73-77, 81.
6. Cuspidi C, Tadic M, Sala C, et al. Blood pressure non-dipping and obstructive sleep apnea syndrome: a meta-analysis. J Clin Med, 2019, 8(9): 1367.
7. 赵建强, 来春林, 刘晓红, 等. 老年高血压病人动态血压曲线下面积与颈动脉硬化关系的探讨. 中西医结合心脑血管病杂志, 2008(7): 767-769.
8. 崔剑, 赵子彦, 赵子婴. 拟合动态血压波动曲线并计算曲线下面积的数学方法. 数理医药学杂志, 2005(1): 5-7.
9. Pinnaduwage D, Beyene J, Fallah S. Genome-wide linkage analysis of systolic blood pressure slope using the genetic analysis workshop 13 data sets. BioMed Central, 2003, 4(1): 1-6.
10. Liu H, Lu R, Shastri S, et al. Assessing extracellular volume in hemodialysis patients using intradialytic blood pressure slopes. Nephron, 2018, 139(2): 120-130.
11. Kelley K, Light R P, Agarwal R. Trended cosinor change model for analyzing hemodynamic rhythm patterns in hemodialysis patients. Hypertension, 2007, 50(1): 143-150.
12. van Rijn-Bikker P C, Snelder N, Ackaert O, et al. Nonlinear mixed effects modeling of the diurnal blood pressure profile in a multiracial population. Am J Hypertens, 2013, 26(9): 1103-1113.
13. Madden J M, Browne L D, Li X, et al. Morning surge in blood pressure using a random effects multiple component cosinor model. Stat Med, 2018, 37(10): 1682-1695.
14. Torres G, Sánchez-de-la-torre M, Martínez-alonso M, et al. Use of ambulatory blood pressure monitoring for the screening of obstructive sleep apnea. J Clin Hypentens, 2015, 17(10): 802-809.
15. Chuiko G P, Dvornik O V, Yaremchuk O, et al. Ambulatory blood pressure monitoring: modeling and data mining. ICTES, 2019: 85-95.
16. 王琳, 韩敏, 王保为, 等. 非高血压OSA患者动态血压相关性研究. 临床耳鼻咽喉头颈外科杂志, 2019, 33(7): 666-669.
17. Caffo B, Diener-West M, Punjabi N M, et al. A novel approach to prediction of mild obstructive sleep disordered breathing in a population-based sample: the sleep heart health study. Sleep, 2010, 33(12): 1641-1648.
18. 陈欢意. 阻塞性睡眠呼吸暂停综合征合并高血压患者的动态血压状况分析. 中国医药科学, 2016, 6(12): 175-178, 209.
19. 黄泰. 高血压患者阻塞性睡眠呼吸暂停综合征筛查评分的建立与验证. 合肥: 安徽医科大学, 2020.
20. Ghulam E M. Modeling unbalanced nested repeated measures data in the presence of informative drop-out with application to ambulatory blood pressure monitoring data. Cincinnati: University of Cincinnati, 2019.
21. 吴巧珍, 陆燕芳, 王伟, 等. 同型半胱氨酸和血压波动对高血压合并阻塞性睡眠呼吸暂停低通气综合征患者的预测价值. 中华医学杂志, 2020, 100(28): 2192-2197.
22. 罗新安, 孙正良. 阻塞性睡眠呼吸暂停低通气综合征的动态血压特征. 山东大学耳鼻喉眼学报, 2006(4): 343-344, 348.
23. Lyshova O V, Kostenko I I. Predictors of obstructive sleep apnea syndrome according to results of clinical examination and ambulatory blood pressure monitoring. Kardiologiya, 2018, 58(9): 12-20.
24. 孙婉璐. 逐波观察阻塞性睡眠呼吸暂停患者睡眠中动态血压变化. 北京: 首都医科大学, 2016.
25. 马春梅, 刘惠亮, 韩玮, 等. 高血压合并阻塞性睡眠呼吸暂停综合征的动态血压及血压节律观察. 武警医学院学报, 2008, 68(5): 402-404.
26. 王慧珍, 陆虹. 高血压合并阻塞性睡眠呼吸暂停的动态血压特点. 天津医药, 2005(12): 775-776.
27. Chung F, Elsaid H. Screening for obstructive sleep apnea before surgery: why is it important?. Curr Opin Anaesthesiol, 2009, 22(3): 405-411.
28. 苗晓东, 潘蕊, 朱东放, 等. 动态心电结合动态血压在筛查睡眠呼吸暂停综合征中的应用. 黑龙江医药, 2015, 28(5): 1143-1145.
29. Furlan S F, Genta-Pereira D C, Omote D D Q, et al. Accuracy of non-dipping blood pressure in predicting obstructive sleep apnea in patients submitted to ambulatory blood pressure monitoring. Am J Respir Crit Care Med, 2017, 195: A2608.
30. 于佳岚, 高国伟. 动态血压监测过程对老年高血压患者睡眠质量及血压监测结果的影响研究. 医学综述, 2013, 19(22): 4205-4208.
31. 李吉. 24h动态血压对高血压患者睡眠质量及血压监测结果的影响. 中外医学研究, 2016, 14(16): 148-149.

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