Exploratory study on quantitative analysis of nocturnal breathing patterns in patients with acute heart failure based on wearable devices_Journal of Biomedical Engineering

Authors：

LI Mengwei ^1,7 , KANG Yu ² , KOU Yuqing ^1,7 , ZHAO Shuanglin ³ , ZHANG Xiu ⁴ , QIU Lirui ⁵ , YAN Wei ⁶ , YU Pengming ⁴ ,  ZHANG Qing ² ,  ZHANG Zhengbo ⁷

1. Medical School of Chinese PLA, Beijing 100853, P. R. China;
2. Department of Cardiology, West China Hospital of Sichuan University, Chengdu 610041, P. R. China;
3. Department of Medical Engineering, the 72nd Group Army Hospital of CPLA, Huzhou, Zhejiang 313000, P. R. China;
4. Rehabilitation Medical Center, West China Hospital of Sichuan University, Chengdu 610041, P. R. China;
5. School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing 100876, P. R. China;
6. Department of Hyperbaric Oxygen, the First Medical Center, Chinese PLA General Hospital, Beijing 100853, P. R. China;
7. Center for Artificial Intelligence in Medicine, Chinese PLA General Hospital, Beijing 100853, P. R. China;

Corresponding author：

ZHANG Qing, Email: qzhang2000cn@163.com; ZHANG Zhengbo, Email: zhengbozhang@126.com

Keywords：

Acute heart failure; Breathing pattern quantification; Wearable devices

DOI：

10.7507/1001-5515.202310015

Video：

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Patients with acute heart failure (AHF) often experience dyspnea, and monitoring and quantifying their breathing patterns can provide reference information for disease and prognosis assessment. In this study, 39 AHF patients and 24 healthy subjects were included. Nighttime chest-abdominal respiratory signals were collected using wearable devices, and the differences in nocturnal breathing patterns between the two groups were quantitatively analyzed. Compared with the healthy group, the AHF group showed a higher mean breathing rate (BR_mean) [(21.03 ± 3.84) beat/min vs. (15.95 ± 3.08) beat/min, P < 0.001], and larger R_RSBI_cv [70.96% (54.34%–104.28)% vs. 58.48% (45.34%–65.95)%, P = 0.005], greater AB_ratio_cv [(22.52 ± 7.14)% vs. (17.10 ± 6.83)%, P = 0.004], and smaller SampEn (0.67 ± 0.37 vs. 1.01 ± 0.29, P < 0.001). Additionally, the mean inspiratory time (TI_mean) and expiration time (TE_mean) were shorter, TI_cv and TE_cv were greater. Furthermore, the LBI_cv was greater, while SD1 and SD2 on the Poincare plot were larger in the AHF group, all of which showed statistically significant differences. Logistic regression calibration revealed that the TI_mean reduction was a risk factor for AHF. The BR_ mean demonstrated the strongest ability to distinguish between the two groups, with an area under the curve (AUC) of 0.846. Parameters such as breathing period, amplitude, coordination, and nonlinear parameters effectively quantify abnormal breathing patterns in AHF patients. Specifically, the reduction in TI_mean serves as a risk factor for AHF, while the BR_mean distinguishes between the two groups. These findings have the potential to provide new information for the assessment of AHF patients.

Citation： LI Mengwei, KANG Yu, KOU Yuqing, ZHAO Shuanglin, ZHANG Xiu, QIU Lirui, YAN Wei, YU Pengming, ZHANG Qing, ZHANG Zhengbo. Exploratory study on quantitative analysis of nocturnal breathing patterns in patients with acute heart failure based on wearable devices. Journal of Biomedical Engineering, 2023, 40(6): 1108-1116. doi: 10.7507/1001-5515.202310015 Copy

1.	Chioncel O, Mebazaa A, Harjola V P, et al. Clinical phenotypes and outcome of patients hospitalized for acute heart failure: The esc heart failure long-term registry. Eur J Heart Fail, 2017, 19(10): 1242-1254.
2.	Stevenson L W, Ross H J, Rathman L D, et al. Remote monitoring for heart failure management at home. J Am Coll Cardiol, 2023, 81(23): 2272-2291.
3.	Carel H. Breathlessness: The rift between objective measurement and subjective experience. Lancet Respir Med, 2018, 6(5): 332-333.
4.	Aderem A. Systems biology: Its practice and challenges. Cell, 2005, 121(4): 511-513.
5.	Van den Bosch O F C, Alvarez-Jimenez R, de Grooth H-J, et al. Breathing variability—implications for anaesthesiology and intensive care. Crit Care, 2021, 25(1): 280.
6.	Greene S J, Mentz R J, Limkakeng A T, et al. Comparison of dyspnea measurement instruments in acute heart failure: The DYSPNEA-AHF pilot study. J Card Fail, 2021, 27(5): 607-609.
7.	Takagawa J, Asanoi H, Tobushi T, et al. Multicenter, prospective study on respiratory stability during recovery from deterioration of chronic heart failure. Circ J, 2018, 83(1): 164-173.
8.	Sakoda M, Asanoi H, Ohtani T, et al. Early detection of worsening heart failure in patients at home using a new telemonitoring system of respiratory stability. Circ J, 2022, 86(7): 1081-1091.
9.	Kumagai N, Dohi K, Fujimoto N, et al. A novel method for the quantitative evaluation of diurnal respiratory instability in patients with heart failure: A pilot study. J Cardiol, 2018, 71(2): 159-167.
10.	Kim J O, Lee D. Detection of abnormal respiration from multiple-input respiratory signals. Sensors (Basel), 2020, 20(10): 2977.
11.	Harrington N, Bui Q M, Wei Z, et al. Passive longitudinal weight and cardiopulmonary monitoring in the home bed. Sci Rep, 2021, 11(1): 24376.
12.	Bennett M K, Shao M, Gorodeski E Z. Home monitoring of heart failure patients at risk for hospital readmission using a novel under-the-mattress piezoelectric sensor: A preliminary single centre experience. J Telemed Telecare, 2017, 23(1): 60-67.
13.	Harbour E, Lasshofer M, Genitrini M, et al. Enhanced breathing pattern detection during running using wearable sensors. Sensors, 2021, 21(16): 5606.
14.	王佳晨, 梁洪, 王雅静, 等. 基于可穿戴系统的呼吸模式参数量化分析研究. 生物医学工程学杂志, 2021, 38(5): 893-902.
15.	Brack T, Thuer I, Clarenbach C F, et al. Daytime cheyne-stokes respiration in ambulatory patients with severe congestive heart failure is associated with increased mortality. Chest, 2007, 132(5): 1463-1471.
16.	Dai Y M N, Redolfi S, Ruttanaumpawan P, et al. Nocturnal rostral fluid shift a unifying concept for the pathogenesis of obstructive and central sleep apnea in men with heart failure. Circulation, 2010, 121(14): 1598-1605.
17.	Xu H, Yan W, Lan K, et al. Assessing electrocardiogram and respiratory signal quality of a wearable device (SensEcho): Semisupervised machine learning-based validation study. JMIR Mhealth Uhealth, 2021, 9(8): e25415.
18.	Khodadad D, Nordebo S, Müller B, et al. Optimized breath detection algorithm in electrical impedance tomography. Physiol Meas, 2018, 39(9): 094001.
19.	Wang J, Yang Z, Wang Y, et al. A quantitative approach and preliminary application in healthy subjects and patients with valvular heart disease for 24-h breathing patterns analysis using wearable devices// 2022 IEEE International Conference on E-health Networking, Application & Services (HealthCom). Genoa: IEEE, 2022: 100-106.
20.	Beydon N, Davis S D, Lombardi E, et al. An official American Thoracic Society/European Respiratory Society statement: Pulmonary function testing in preschool children. Am J Respir Crit Care Med, 2007, 175(12): 1304-1345.
21.	Kamen P W, Tonkin A M. Application of the poincare plot to heart rate variability: A new measure of functional status in heart failure. Aust N Z J Med, 1995, 25(1): 18-26.
22.	Veiga J, Faria R C P, Esteves G P, et al. Approximate entropy as a measure of the airflow pattern complexity in asthma// 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology. Buenos Aires: IEEE, 2010, 2463-2466.
23.	Richman J S, Moorman J R. Physiological time-series analysis using approximate entropy and sample entropy. Am J Physiol-Heart C, 2000, 278(6): H2039-H2049.
24.	Corra U, Giordano A, Bosimini E, et al. Oscillatory ventilation during exercise in patients with chronic heart failure: Clinical correlates and prognostic implications. Chest, 2002, 121(5): 1572-1580.
25.	Naughton M T. Cheyne-Stokes respiration: friend or foe? Thorax, 2012, 67(4): 357-360.
26.	Troy J C, Surendan S, Kenneth C B, et al. The resistive and elastic work of breathing during exercise in patients with chronic heart failure. Eur Respir J, 2012, 39(6): 1449-1457.
27.	Cross T J, Kim C H, Johnson B D, et al. The interactions between respiratory and cardiovascular systems in systolic heart failure. J Appl Physiol (1985), 2020, 128(1): 214-224.
28.	Yamauchi M, Tamaki S, Yoshikawa M, et al. Differences in breathing patterning during wakefulness in patients with mixed apnea-dominant vs obstructive-dominant sleep apnea. Chest, 2011, 140(1): 54-61.
29.	Raoufy M R, Ghafari T, Darooei R, et al. Classification of asthma based on nonlinear analysis of breathing pattern. PLoS One, 2016, 11(1): e0147976.
30.	Tsai C H, Ma H P, Lin Y T, et al. Usefulness of heart rhythm complexity in heart failure detection and diagnosis. Sci Rep, 2020, 10(1): 14916.
31.	陈志高, 孙兴国, 黄洁. 心力衰竭相关异常呼吸的新机制、临床意义及治疗策略. 中国循环杂志, 2015, 30(11): 1128-1130.

1. Chioncel O, Mebazaa A, Harjola V P, et al. Clinical phenotypes and outcome of patients hospitalized for acute heart failure: The esc heart failure long-term registry. Eur J Heart Fail, 2017, 19(10): 1242-1254.
2. Stevenson L W, Ross H J, Rathman L D, et al. Remote monitoring for heart failure management at home. J Am Coll Cardiol, 2023, 81(23): 2272-2291.
3. Carel H. Breathlessness: The rift between objective measurement and subjective experience. Lancet Respir Med, 2018, 6(5): 332-333.
4. Aderem A. Systems biology: Its practice and challenges. Cell, 2005, 121(4): 511-513.
5. Van den Bosch O F C, Alvarez-Jimenez R, de Grooth H-J, et al. Breathing variability—implications for anaesthesiology and intensive care. Crit Care, 2021, 25(1): 280.
6. Greene S J, Mentz R J, Limkakeng A T, et al. Comparison of dyspnea measurement instruments in acute heart failure: The DYSPNEA-AHF pilot study. J Card Fail, 2021, 27(5): 607-609.
7. Takagawa J, Asanoi H, Tobushi T, et al. Multicenter, prospective study on respiratory stability during recovery from deterioration of chronic heart failure. Circ J, 2018, 83(1): 164-173.
8. Sakoda M, Asanoi H, Ohtani T, et al. Early detection of worsening heart failure in patients at home using a new telemonitoring system of respiratory stability. Circ J, 2022, 86(7): 1081-1091.
9. Kumagai N, Dohi K, Fujimoto N, et al. A novel method for the quantitative evaluation of diurnal respiratory instability in patients with heart failure: A pilot study. J Cardiol, 2018, 71(2): 159-167.
10. Kim J O, Lee D. Detection of abnormal respiration from multiple-input respiratory signals. Sensors (Basel), 2020, 20(10): 2977.
11. Harrington N, Bui Q M, Wei Z, et al. Passive longitudinal weight and cardiopulmonary monitoring in the home bed. Sci Rep, 2021, 11(1): 24376.
12. Bennett M K, Shao M, Gorodeski E Z. Home monitoring of heart failure patients at risk for hospital readmission using a novel under-the-mattress piezoelectric sensor: A preliminary single centre experience. J Telemed Telecare, 2017, 23(1): 60-67.
13. Harbour E, Lasshofer M, Genitrini M, et al. Enhanced breathing pattern detection during running using wearable sensors. Sensors, 2021, 21(16): 5606.
14. 王佳晨, 梁洪, 王雅静, 等. 基于可穿戴系统的呼吸模式参数量化分析研究. 生物医学工程学杂志, 2021, 38(5): 893-902.
15. Brack T, Thuer I, Clarenbach C F, et al. Daytime cheyne-stokes respiration in ambulatory patients with severe congestive heart failure is associated with increased mortality. Chest, 2007, 132(5): 1463-1471.
16. Dai Y M N, Redolfi S, Ruttanaumpawan P, et al. Nocturnal rostral fluid shift a unifying concept for the pathogenesis of obstructive and central sleep apnea in men with heart failure. Circulation, 2010, 121(14): 1598-1605.
17. Xu H, Yan W, Lan K, et al. Assessing electrocardiogram and respiratory signal quality of a wearable device (SensEcho): Semisupervised machine learning-based validation study. JMIR Mhealth Uhealth, 2021, 9(8): e25415.
18. Khodadad D, Nordebo S, Müller B, et al. Optimized breath detection algorithm in electrical impedance tomography. Physiol Meas, 2018, 39(9): 094001.
19. Wang J, Yang Z, Wang Y, et al. A quantitative approach and preliminary application in healthy subjects and patients with valvular heart disease for 24-h breathing patterns analysis using wearable devices// 2022 IEEE International Conference on E-health Networking, Application & Services (HealthCom). Genoa: IEEE, 2022: 100-106.
20. Beydon N, Davis S D, Lombardi E, et al. An official American Thoracic Society/European Respiratory Society statement: Pulmonary function testing in preschool children. Am J Respir Crit Care Med, 2007, 175(12): 1304-1345.
21. Kamen P W, Tonkin A M. Application of the poincare plot to heart rate variability: A new measure of functional status in heart failure. Aust N Z J Med, 1995, 25(1): 18-26.
22. Veiga J, Faria R C P, Esteves G P, et al. Approximate entropy as a measure of the airflow pattern complexity in asthma// 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology. Buenos Aires: IEEE, 2010, 2463-2466.
23. Richman J S, Moorman J R. Physiological time-series analysis using approximate entropy and sample entropy. Am J Physiol-Heart C, 2000, 278(6): H2039-H2049.
24. Corra U, Giordano A, Bosimini E, et al. Oscillatory ventilation during exercise in patients with chronic heart failure: Clinical correlates and prognostic implications. Chest, 2002, 121(5): 1572-1580.
25. Naughton M T. Cheyne-Stokes respiration: friend or foe? Thorax, 2012, 67(4): 357-360.
26. Troy J C, Surendan S, Kenneth C B, et al. The resistive and elastic work of breathing during exercise in patients with chronic heart failure. Eur Respir J, 2012, 39(6): 1449-1457.
27. Cross T J, Kim C H, Johnson B D, et al. The interactions between respiratory and cardiovascular systems in systolic heart failure. J Appl Physiol (1985), 2020, 128(1): 214-224.
28. Yamauchi M, Tamaki S, Yoshikawa M, et al. Differences in breathing patterning during wakefulness in patients with mixed apnea-dominant vs obstructive-dominant sleep apnea. Chest, 2011, 140(1): 54-61.
29. Raoufy M R, Ghafari T, Darooei R, et al. Classification of asthma based on nonlinear analysis of breathing pattern. PLoS One, 2016, 11(1): e0147976.
30. Tsai C H, Ma H P, Lin Y T, et al. Usefulness of heart rhythm complexity in heart failure detection and diagnosis. Sci Rep, 2020, 10(1): 14916.
31. 陈志高, 孙兴国, 黄洁. 心力衰竭相关异常呼吸的新机制、临床意义及治疗策略. 中国循环杂志, 2015, 30(11): 1128-1130.

Journal of Biomedical Engineering

Exploratory study on quantitative analysis of nocturnal breathing patterns in patients with acute heart failure based on wearable devices

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