Quantitative analysis of breathing patterns based on wearable systems_Journal of Biomedical Engineering

Authors：

WANG Jiachen ¹ , LIANG Hong ² , WANG Yajing ³ , WANG Weitao ⁴ , LAN Ke ⁵ , CAO Lu ⁶ ,  ZHANG Zhengbo ^2,7 ,  LI Yuzhu ⁶ , LIU Zhiwen ³ , CAO Desen ^2,7

1. Medical School of Chinese PLA, Beijing 100853, P.R.China;
2. Department of Biomedical Engineering, Chinese PLA General Hospital, Beijing 100853, P.R.China;
3. School of Information and Electronics, Beijing Institute of Technology, Beijing 100081, P.R.China;
4. School of Computer Science and Engineering, Southeast University, Nanjing 211189, P.R.China;
5. Beijing Sensecho Science & Technology Co. Ltd, Beijing 100853, P.R.China;
6. Pneumology Department, Chinese PLA General Hospital, Beijing 100853, P.R.China;
7. R&D and Evaluation Center for Medical Devices, Chinese PLA General Hospital, Beijing 100853, P.R.China;

Corresponding author：

ZHANG Zhengbo, Email: zhengbozhang@126.com; LI Yuzhu, Email: lyz301@163.com

Keywords：

breathing pattern; wearable system; respiratory inductive plethysmography; signal quality; chronic obstructive pulmonary disease

DOI：

10.7507/1001-5515.202004047

Video：

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Abstract Full text Figures/Tables Video References Cited by

Breathing pattern parameters refer to the characteristic pattern parameters of respiratory movements, including the breathing amplitude and cycle, chest and abdomen contribution, coordination, etc. It is of great importance to analyze the breathing pattern parameters quantificationally when exploring the pathophysiological variations of breathing and providing instructions on pulmonary rehabilitation training. Our study provided detailed method to quantify breathing pattern parameters including respiratory rate, inspiratory time, expiratory time, inspiratory time proportion, tidal volume, chest respiratory contribution ratio, thoracoabdominal phase difference and peak inspiratory flow. We also brought in “respiratory signal quality index” to deal with the quality evaluation and quantification analysis of long-term thoracic-abdominal respiratory movement signal recorded, and proposed the way of analyzing the variance of breathing pattern parameters. On this basis, we collected chest and abdomen respiratory movement signals in 23 chronic obstructive pulmonary disease (COPD) patients and 22 normal pulmonary function subjects under spontaneous state in a 15 minute-interval using portable cardio-pulmonary monitoring system. We then quantified subjects’ breathing pattern parameters and variability. The results showed great difference between the COPD patients and the controls in terms of respiratory rate, inspiratory time, expiratory time, thoracoabdominal phase difference and peak inspiratory flow. COPD patients also showed greater variance of breathing pattern parameters than the controls, and unsynchronized thoracic-abdominal movements were even observed among several patients. Therefore, the quantification and analyzing method of breathing pattern parameters based on the portable cardiopulmonary parameters monitoring system might assist the diagnosis and assessment of respiratory system diseases and hopefully provide new parameters and indexes for monitoring the physical status of patients with cardiopulmonary disease.

Citation： WANG Jiachen, LIANG Hong, WANG Yajing, WANG Weitao, LAN Ke, CAO Lu, ZHANG Zhengbo, LI Yuzhu, LIU Zhiwen, CAO Desen. Quantitative analysis of breathing patterns based on wearable systems. Journal of Biomedical Engineering, 2021, 38(5): 893-902. doi: 10.7507/1001-5515.202004047 Copy

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2.	Rubio N, Parker R A, Drost E M, et al. Home monitoring of breathing rate in people with chronic obstructive pulmonary disease: observational study of feasibility, acceptability, and change after exacerbation. Int J Chronic Obsrt, 2017, 12: 1221-1231.
3.	Panaite V, Hindash A C, Bylsma L M, et al. Respiratory sinus arrhythmia reactivity to a sad film predicts depression symptom improvement and symptomatic trajectory. Int J Psychophysiol, 2016, 99: 108-113.
4.	Spathis A, Booth S, Moffat C, et al. The Breathing, Thinking, Functioning clinical model: a proposal to facilitate evidence-based breathlessness management in chronic respiratory disease. NPJ Prim Care Resp Med, 2017, 27(1): 27.
5.	Rétory Y, David P, Picciotto C D, et al. Validity of thoracic respiratory inductive plethysmography in high body mass index subjects. Respir Physiol Neurobiol, 2017, 242: 52-58.
6.	Tehrany R, DeVos R, Bruton A. Breathing pattern recordings using respiratory inductive plethysmography, before and after a physiotherapy breathing retraining program for asthma: A case report. Physiother Theory Pract, 2018, 34(4): 329-335.
7.	Motamedi-Fakhr S, Iles R, Barney A, et al. Evaluation of the agreement of tidal breathing parameters measured simultaneously using pneumotachography and structured light plethysmography. Physiol Rep, 2017, 5(3): e13124.
8.	张政波, 毕亚琼, 俞梦孙, 等. 穿戴式呼吸感应体积描记用于睡眠呼吸事件检测. 生物医学工程学杂志, 2008, 25(2): 318-322.
9.	Peng C K, Mietus J E, Liu Y, et al. Quantifying fractal dynamics of human respiration: Age and gender effects. Ann Biomed Eng, 2002, 30(5): 683-692.
10.	张叶钦, 胡晓芸, 侯飞飞, 等. 早期慢性阻塞性肺疾病诊治进展. 临床肺科杂志, 2018, 23(3): 166-169.
11.	叶伟杰, 莫蝶仪. 慢阻肺急性加重期与慢阻肺合并社区获得性肺炎患者临床表现对比分析. 内科, 2017, 12(6): 756-759.
12.	曹德森, 李德玉, 张政波, 等. 随行生理监护系统设计及性能初步验证. 生物医学工程学杂志, 2019, 36(1): 121-130.
13.	张政波, 俞梦孙, 李若新, 等. 背心式呼吸感应体积描记系统设计. 航天医学与医学工程, 2006, 19(5): 377-381.
14.	Sakoe H, Chiba S. Dynamic programming algorithm optimization for spoken word recognition. IEEE Trans Acoust Speech Signal Process, 1978, 26(1): 43-49.
15.	Konno K, Mead J. Measurement of the separate volume changes of rib cage and abdomen during breathing. J Appl Physiol, 1967, 22(3): 407-422.
16.	Sackner M A, Watson H, Belsito A S, et al. Calibration of respiratory inductive plethysmograph during natural breathing. J Appl Physiol, 1989, 66(1): 410-420.
17.	Houssein A, Ge D, Gastinger S, et al. Estimation of respiratory variables from thoracoabdominal breathing distance: a review of different techniques and calibration methods. Physiol Meas, 2019, 40(3): 03TR01.
18.	张政波, 俞梦孙, 李若新, 等. 用背心式呼吸感应体积描记系统实现通气量无创测量. 生物医学工程研究, 2005, 24(4): 203-208.
19.	Prisk G K, Hammer J, Newth C J L. Techniques for measurement of thoracoabdominal asynchrony. Pediatr Pulm, 2002, 34(6): 462-472.
20.	Wasserstein R L, Lazar N A. The ASA's Statement on p-Values: Context, Process, and Purpose. Am Stat, 2016, 70(2): 129-133.
21.	Ghosh S, Pleasants R A, Ohar J A, et al. Prevalence and factors associated with suboptimal peak inspiratory flow rates in COPD. Int J Chronic Obstr, 2019, 14(1): 585-595.
22.	Massaroni C, Nicolò A, Schena E, et al. Remote respiratory monitoring in the time of COVID-19. Front Physiol, 2020, 11: 635.
23.	Aysin B, Aysin E. Effect of respiration in heart rate variability (HRV) analysis//2006 International Conference of IEEE Engineering in Medicine and Biology Society. New York: IEEE, 2006: 1776-1779.
24.	Druschky K, Lorenz J, Druschky A. Effects of respiratory rate on heart rate variability in neurologic outpatients with epilepsies or migraine: A preliminary study. Med Prin Pract, 2020, 29(4): 318-325.

1. Pisani L, Fasano L, Corcione N, et al. Change in pulmonary mechanics and the effect on breathing pattern of high flow oxygen therapy in stable hypercapnic COPD. Thorax, 2017, 72(4): 373-375.
2. Rubio N, Parker R A, Drost E M, et al. Home monitoring of breathing rate in people with chronic obstructive pulmonary disease: observational study of feasibility, acceptability, and change after exacerbation. Int J Chronic Obsrt, 2017, 12: 1221-1231.
3. Panaite V, Hindash A C, Bylsma L M, et al. Respiratory sinus arrhythmia reactivity to a sad film predicts depression symptom improvement and symptomatic trajectory. Int J Psychophysiol, 2016, 99: 108-113.
4. Spathis A, Booth S, Moffat C, et al. The Breathing, Thinking, Functioning clinical model: a proposal to facilitate evidence-based breathlessness management in chronic respiratory disease. NPJ Prim Care Resp Med, 2017, 27(1): 27.
5. Rétory Y, David P, Picciotto C D, et al. Validity of thoracic respiratory inductive plethysmography in high body mass index subjects. Respir Physiol Neurobiol, 2017, 242: 52-58.
6. Tehrany R, DeVos R, Bruton A. Breathing pattern recordings using respiratory inductive plethysmography, before and after a physiotherapy breathing retraining program for asthma: A case report. Physiother Theory Pract, 2018, 34(4): 329-335.
7. Motamedi-Fakhr S, Iles R, Barney A, et al. Evaluation of the agreement of tidal breathing parameters measured simultaneously using pneumotachography and structured light plethysmography. Physiol Rep, 2017, 5(3): e13124.
8. 张政波, 毕亚琼, 俞梦孙, 等. 穿戴式呼吸感应体积描记用于睡眠呼吸事件检测. 生物医学工程学杂志, 2008, 25(2): 318-322.
9. Peng C K, Mietus J E, Liu Y, et al. Quantifying fractal dynamics of human respiration: Age and gender effects. Ann Biomed Eng, 2002, 30(5): 683-692.
10. 张叶钦, 胡晓芸, 侯飞飞, 等. 早期慢性阻塞性肺疾病诊治进展. 临床肺科杂志, 2018, 23(3): 166-169.
11. 叶伟杰, 莫蝶仪. 慢阻肺急性加重期与慢阻肺合并社区获得性肺炎患者临床表现对比分析. 内科, 2017, 12(6): 756-759.
12. 曹德森, 李德玉, 张政波, 等. 随行生理监护系统设计及性能初步验证. 生物医学工程学杂志, 2019, 36(1): 121-130.
13. 张政波, 俞梦孙, 李若新, 等. 背心式呼吸感应体积描记系统设计. 航天医学与医学工程, 2006, 19(5): 377-381.
14. Sakoe H, Chiba S. Dynamic programming algorithm optimization for spoken word recognition. IEEE Trans Acoust Speech Signal Process, 1978, 26(1): 43-49.
15. Konno K, Mead J. Measurement of the separate volume changes of rib cage and abdomen during breathing. J Appl Physiol, 1967, 22(3): 407-422.
16. Sackner M A, Watson H, Belsito A S, et al. Calibration of respiratory inductive plethysmograph during natural breathing. J Appl Physiol, 1989, 66(1): 410-420.
17. Houssein A, Ge D, Gastinger S, et al. Estimation of respiratory variables from thoracoabdominal breathing distance: a review of different techniques and calibration methods. Physiol Meas, 2019, 40(3): 03TR01.
18. 张政波, 俞梦孙, 李若新, 等. 用背心式呼吸感应体积描记系统实现通气量无创测量. 生物医学工程研究, 2005, 24(4): 203-208.
19. Prisk G K, Hammer J, Newth C J L. Techniques for measurement of thoracoabdominal asynchrony. Pediatr Pulm, 2002, 34(6): 462-472.
20. Wasserstein R L, Lazar N A. The ASA's Statement on p-Values: Context, Process, and Purpose. Am Stat, 2016, 70(2): 129-133.
21. Ghosh S, Pleasants R A, Ohar J A, et al. Prevalence and factors associated with suboptimal peak inspiratory flow rates in COPD. Int J Chronic Obstr, 2019, 14(1): 585-595.
22. Massaroni C, Nicolò A, Schena E, et al. Remote respiratory monitoring in the time of COVID-19. Front Physiol, 2020, 11: 635.
23. Aysin B, Aysin E. Effect of respiration in heart rate variability (HRV) analysis//2006 International Conference of IEEE Engineering in Medicine and Biology Society. New York: IEEE, 2006: 1776-1779.
24. Druschky K, Lorenz J, Druschky A. Effects of respiratory rate on heart rate variability in neurologic outpatients with epilepsies or migraine: A preliminary study. Med Prin Pract, 2020, 29(4): 318-325.

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Quantitative analysis of breathing patterns based on wearable systems

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