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

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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.	潘悅達, 王東博, 韓德民. 我國成人阻塞性睡眠呼吸暫停低通氣綜合征患病率的Meta分析. 醫學信息, 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.	趙建強, 來春林, 劉曉紅, 等. 老年高血壓病人動態血壓曲線下面積與頸動脈硬化關系的探討. 中西醫結合心腦血管病雜志, 2008(7): 767-769.
8.	崔劍, 趙子彥, 趙子嬰. 擬合動態血壓波動曲線并計算曲線下面積的數學方法. 數理醫藥學雜志, 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, 韓敏, 王保為, 等. 非高血壓OSA患者動態血壓相關性研究. 臨床耳鼻咽喉頭頸外科雜志, 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.	陳歡意. 阻塞性睡眠呼吸暫停綜合征合并高血壓患者的動態血壓狀況分析. 中國醫藥科學, 2016, 6(12): 175-178, 209.
19.	黃泰. 高血壓患者阻塞性睡眠呼吸暫停綜合征篩查評分的建立與驗證. 合肥: 安徽醫科大學, 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.	吳巧珍, 陸燕芳, 王偉, 等. 同型半胱氨酸和血壓波動對高血壓合并阻塞性睡眠呼吸暫停低通氣綜合征患者的預測價值. 中華醫學雜志, 2020, 100(28): 2192-2197.
22.	羅新安, 孫正良. 阻塞性睡眠呼吸暫停低通氣綜合征的動態血壓特征. 山東大學耳鼻喉眼學報, 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.	孫婉璐. 逐波觀察阻塞性睡眠呼吸暫停患者睡眠中動態血壓變化. 北京: 首都醫科大學, 2016.
25.	馬春梅, 劉惠亮, 韓瑋, 等. 高血壓合并阻塞性睡眠呼吸暫停綜合征的動態血壓及血壓節律觀察. 武警醫學院學報, 2008, 68(5): 402-404.
26.	王慧珍, 陸虹. 高血壓合并阻塞性睡眠呼吸暫停的動態血壓特點. 天津醫藥, 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.	苗曉東, 潘蕊, 朱東放, 等. 動態心電結合動態血壓在篩查睡眠呼吸暫停綜合征中的應用. 黑龍江醫藥, 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.	于佳嵐, 高國偉. 動態血壓監測過程對老年高血壓患者睡眠質量及血壓監測結果的影響研究. 醫學綜述, 2013, 19(22): 4205-4208.
31.	李吉. 24h動態血壓對高血壓患者睡眠質量及血壓監測結果的影響. 中外醫學研究, 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. 潘悅達, 王東博, 韓德民. 我國成人阻塞性睡眠呼吸暫停低通氣綜合征患病率的Meta分析. 醫學信息, 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. 趙建強, 來春林, 劉曉紅, 等. 老年高血壓病人動態血壓曲線下面積與頸動脈硬化關系的探討. 中西醫結合心腦血管病雜志, 2008(7): 767-769.
8. 崔劍, 趙子彥, 趙子嬰. 擬合動態血壓波動曲線并計算曲線下面積的數學方法. 數理醫藥學雜志, 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, 韓敏, 王保為, 等. 非高血壓OSA患者動態血壓相關性研究. 臨床耳鼻咽喉頭頸外科雜志, 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. 陳歡意. 阻塞性睡眠呼吸暫停綜合征合并高血壓患者的動態血壓狀況分析. 中國醫藥科學, 2016, 6(12): 175-178, 209.
19. 黃泰. 高血壓患者阻塞性睡眠呼吸暫停綜合征篩查評分的建立與驗證. 合肥: 安徽醫科大學, 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. 吳巧珍, 陸燕芳, 王偉, 等. 同型半胱氨酸和血壓波動對高血壓合并阻塞性睡眠呼吸暫停低通氣綜合征患者的預測價值. 中華醫學雜志, 2020, 100(28): 2192-2197.
22. 羅新安, 孫正良. 阻塞性睡眠呼吸暫停低通氣綜合征的動態血壓特征. 山東大學耳鼻喉眼學報, 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. 孫婉璐. 逐波觀察阻塞性睡眠呼吸暫停患者睡眠中動態血壓變化. 北京: 首都醫科大學, 2016.
25. 馬春梅, 劉惠亮, 韓瑋, 等. 高血壓合并阻塞性睡眠呼吸暫停綜合征的動態血壓及血壓節律觀察. 武警醫學院學報, 2008, 68(5): 402-404.
26. 王慧珍, 陸虹. 高血壓合并阻塞性睡眠呼吸暫停的動態血壓特點. 天津醫藥, 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. 苗曉東, 潘蕊, 朱東放, 等. 動態心電結合動態血壓在篩查睡眠呼吸暫停綜合征中的應用. 黑龍江醫藥, 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. 于佳嵐, 高國偉. 動態血壓監測過程對老年高血壓患者睡眠質量及血壓監測結果的影響研究. 醫學綜述, 2013, 19(22): 4205-4208.
31. 李吉. 24h動態血壓對高血壓患者睡眠質量及血壓監測結果的影響. 中外醫學研究, 2016, 14(16): 148-149.

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