Study on the quality evaluation of electrocardiogram signal in wearable physiological monitoring system_Journal of Biomedical Engineering

Authors：

HAN Ning ¹ , LAN Ke ² , ZHANG Yuezhou ² , WAN Tao ³ ,  ZHANG Zhengbo ^1,4,5 , CAO Deshen ^1,4 , YAN Wei ⁶

1. Department of Biomedical Engineering, Chinese PLA General Hospital, Beijing 100853, P.R.China;
2. SensEcho Co, Ltd, Beijing 110000, P.R.China;
3. College of Biological and Medical Engineering, BeihangUniversity, Beijing 100091, P.R.China;
4. Medical Device R&D and Evaluation Center, Chinese PLA General Hospital, Beijing 100853, P.R.China;
5. Medical Big Data Center, Chinese PLA General Hospital, Beijing 100853, P.R.China;
6. Department of Hyperbaric Oxygen, Chinese PLA General Hospital, Beijing 100853, P.R.China;

Corresponding author：

ZHANG Zhengbo, Email: zhengbozhang@126.com

Keywords：

wearable physiological monitoring system; electrocardiogram signal; signal quality; evaluation algorithm

DOI：

10.7507/1001-5515.201909012

Video：

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As a novel technology, wearable physiological parameter monitoring technology represents the future of monitoring technology. However, there are still many problems in the application of this kind of technology. In this paper, a pilot study was conducted to evaluate the quality of electrocardiogram (ECG) signals of the wearable physiological monitoring system (SensEcho-5B). Firstly, an evaluation algorithm of ECG signal quality was developed based on template matching method, which was used for automatic and quantitative evaluation of ECG signals. The algorithm performance was tested on a randomly selected 100 h dataset of ECG signals from 100 subjects (15 healthy subjects and 85 patients with cardiovascular diseases). On this basis, 24-hour ECG data of 30 subjects (7 healthy subjects and 23 patients with cardiovascular diseases) were collected synchronously by SensEcho-5B and ECG Holter. The evaluation algorithm was used to evaluate the quality of ECG signals recorded synchronously by the two systems. Algorithm validation results: sensitivity was 100%, specificity was 99.51%, and accuracy was 99.99%. Results of controlled test of 30 subjects: the median (Q1, Q3) of ECG signal detected by SensEcho-5B with poor signal quality time was 8.93 (0.84, 32.53) minutes, and the median (Q1, Q3) of ECG signal detected by Holter with poor signal quality time was 14.75 (4.39, 35.98) minutes (Rank sum test, P=0.133). The results show that the ECG signal quality algorithm proposed in this paper can effectively evaluate the ECG signal quality of the wearable physiological monitoring system. Compared with signal measured by Holter, the ECG signal measured by SensEcho-5B has the same ECG signal quality. Follow-up studies will further collect physiological data of large samples in real clinical environment, analyze and evaluate the quality of ECG signals, so as to continuously optimize the performance of the monitoring system.

Citation： HAN Ning, LAN Ke, ZHANG Yuezhou, WAN Tao, ZHANG Zhengbo, CAO Deshen, YAN Wei. Study on the quality evaluation of electrocardiogram signal in wearable physiological monitoring system. Journal of Biomedical Engineering, 2021, 38(1): 131-137. doi: 10.7507/1001-5515.201909012 Copy

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25.	Birrenkott D. Respiratory quality index design and validation for ECG and PPG derived respiratory data. Report for transfer of status. Oxford: University of Oxford, 2015.
26.	Satija U, Ramkumar B, Manikandan M S. An automated ECG signal quality assessment method for unsupervised diagnostic systems. Biocyber Biomed Eng, 2018, 38(1): 54-70.
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28.	易晓霖, 吴怡之. 基于模糊综合评价的可穿戴心电信号质量评估. 计算机应用, 2011, 31(12): 3438-3440.
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30.	Lifford G D, Azuaje F, McSharry P. Advanced methods and tools for ECG data analysis. Norwood: Artech House, 2006: 69-70.

1. 鲁燕燕, 谢红珍. 可穿戴设备在医疗领域的应用. 中国医疗器械杂志, 2017, 41(3): 213-215.
2. Grossman P. TheLifeShirt: a multi-function ambulatory system monitoring health, disease, and medical intervention in the real world. Stud Health Technol Inform, 2004, 108: 133-41.
3. 颜延, 邹浩, 周林, 等. 可穿戴技术的发展. 中国生物医学工程学报, 2015, 34(6): 644-653.
4. 张政波, 俞梦孙, 赵显亮, 等. 穿戴式多参数协同监测系统设计. 航天医学与医学工程, 2008, 21(1): 66-69.
5. 滕晓菲, 张元亭. 移动医疗: 穿戴式医疗仪器的发展趋势. 中国医疗器械杂志, 2006, 30(5): 330-341.
6. Pandian P S, Mohanavelu K, Safeer K P. SmartVest: wearable multi-parameter remote physiological monitoring system. MedEng Phys, 2008, 30(4): 466-477.
7. Shen Y H, Zheng J W, Zhang Z B, et al. Design and implementation of a wearable, multiparameter physiological monitoring system for the study of human heat stress, cold stress, and thermal comfort. Instrum Sci Technol, 2012, 40(4): 290-304.
8. 樊瑜波. 可穿戴医疗/健康技术——生物医学工程的机遇和挑战. 生物医学工程学杂志, 2016, 33(1): 1.
9. 郭劲松, 邓亲恺. 可穿戴式心电、呼吸传感器与检测系统的研制. 中国医疗器械杂志, 2006, 30(5): 341-344.
10. Zhang Z B, Shen Y H, Wang W D, et al. Design and implementation of sensing shirt for ambulatory cardiopulmonary monitoring. J Med Biol Eng, 2011, 31(3): 207-216.
11. Zhang Z, Zheng J. Development of a respiratory inductive plethysmography module supporting multiple sensors for wearable systems. Sensors (Basel), 2012, 12(10): 13167-13184.
12. 李桥. 危重病人生命体征信号质量评估与分析. 济南: 山东大学, 2008.
13. Wang J Y. A new method for evaluating ECG signal quality for multi-1ead arrhythmia analysis// Computers in Cardiology. Memphis: IEEE, 2002: 85-88.
14. Langley P, Marco L Y D, King S, et al. An algorithm for assessment of quality of ECGs acquired via mobile telephones// Computing in Cardiology. Hangzhou: IEEE, 2011: 281-284.
15. Chudacek V, Zach L, Kuzilek J, et al. Simple scoring system for ECG quality assessment on Android platform// Computing in Cardiology. Hangzhou: IEEE, 2011: 449-451.
16. Xia Henian, Gracia G A, McBride J C. Computer algorithms for evaluating the quality of ECGs in real time// Computing in Cardiology. Hangzhou: IEEE, 2011: 369-372.
17. 李延军, 唐晓英, 许志, 等. 一种基于波形特征的12导联心电信号质量的估计方法. 航天医学与医学工程, 2015, 28(6): 419-426.
18. Sabarimalai Manikandan M, Dandapat S. Wavelet energy based diagnostic distortion measure for ECG. Biomed Signal Process Control, 2007, 2: 80-96.
19. Kalkstein N, Kinar Y, Na'aman M, et al. Using machine learning to detect problems in ECG Data collection// Computing in Cardioloy. Hangzhou: IEEE, 2011: 437-440.
20. Zaunseder S, Huhle R, Malberg H. CinC challenge assessing the usability of ECG by ensemble decision trees// Computing in Cardiology. Hangzhou: IEEE, 2011: 277-280.
21. 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.
22. Li Q, Rajagopalan C, Clifford G D. A machine learning approach to multi-level ECG signal quality classification. Comput Meth Prog Bio, 2014, 117(3): 435-447.
23. Birrenkott D A, Pimentel M A F, Watkinson P J, et al. Robust estimation of respiratory rate via ECG-and PPG-derived respiratory quality indices// 2016 IEEE 38th Annual International Conference of the Engineering in Medicine and Biology Society (EMBC). Orlando: IEEE, 2016: 676-679.
24. Satija U, Ramkumar B, Manikandan M S. Real-time signal quality-aware ECG telemetry system for IoT-based health care monitoring. IEEE Internet Things J, 2017, 4(3): 815-823.
25. Birrenkott D. Respiratory quality index design and validation for ECG and PPG derived respiratory data. Report for transfer of status. Oxford: University of Oxford, 2015.
26. Satija U, Ramkumar B, Manikandan M S. An automated ECG signal quality assessment method for unsupervised diagnostic systems. Biocyber Biomed Eng, 2018, 38(1): 54-70.
27. Hamilton P. Open source ECG analysis// Computers in Cardiology. Memphis: IEEE, 2002: 101-104.
28. 易晓霖, 吴怡之. 基于模糊综合评价的可穿戴心电信号质量评估. 计算机应用, 2011, 31(12): 3438-3440.
29. He T, Clifford G, Tarassenko L. Application of independent component analysis in removing artifacts from the melectrocardiogram. Neur Comput Appl, 2006, 15: 105-116.
30. Lifford G D, Azuaje F, McSharry P. Advanced methods and tools for ECG data analysis. Norwood: Artech House, 2006: 69-70.

Journal of Biomedical Engineering

Study on the quality evaluation of electrocardiogram signal in wearable physiological monitoring system

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