A heart rate detection method for wearable electrocardiogram with the presence of motion interference_Journal of Biomedical Engineering

Authors：

XIE Jialing ¹ , GONG Yushun ¹ , WEI Liang ¹ , WANG Juan ² , LI Weiming ¹ ,  LI Yongqin ¹

1. Department of Biomedical Engineering and Imaging Medicine, Army Medical University, Chongqing 400038, P.R.China;
2. Emergency Department, First Affiliated Hospital of Army Medical University, Chongqing 400038, P.R.China;

Corresponding author：

LI Yongqin, Email: lyq@tmmu.edu.cn

Keywords：

wearable device; electrocardiogram; motion interference; heart rate detection

DOI：

10.7507/1001-5515.202011011

Video：

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The dynamic electrocardiogram (ECG) collected by wearable devices is often corrupted by motion interference due to human activities. The frequency of the interference and the frequency of the ECG signal overlap with each other, which distorts and deforms the ECG signal, and then affects the accuracy of heart rate detection. In this paper, a heart rate detection method that using coarse graining technique was proposed. First, the ECG signal was preprocessed to remove the baseline drift and the high-frequency interference. Second, the motion-related high amplitude interference exceeding the preset threshold was suppressed by signal compression method. Third, the signal was coarse-grained by adaptive peak dilation and waveform reconstruction. Heart rate was calculated based on the frequency spectrum obtained from fast Fourier transformation. The performance of the method was compared with a wavelet transform based QRS feature extraction algorithm using ECG collected from 30 volunteers at rest and in different motion states. The results showed that the correlation coefficient between the calculated heart rate and the standard heart rate was 0.999, which was higher than the result of the wavelet transform method (r = 0.971). The accuracy of the proposed method was significantly higher than the wavelet transform method in all states, including resting (99.95% vs. 99.14%, P < 0.01), walking (100% vs. 97.26%, P < 0.01) and running (100% vs. 90.89%, P < 0.01). The absolute error [0 (0, 1) vs. 1 (0, 1), P < 0.05] and relative error [0 (0, 0.59) vs. 0.52 (0, 0.72), P < 0.05] of the proposed method were significantly lower than the wavelet transform method during running state. The method presented in this paper shows high accuracy and strong anti-interference ability, and is potentially used in wearable devices to realize real-time continuous heart rate monitoring in daily activities and exercise conditions.

Citation： XIE Jialing, GONG Yushun, WEI Liang, WANG Juan, LI Weiming, LI Yongqin. A heart rate detection method for wearable electrocardiogram with the presence of motion interference. Journal of Biomedical Engineering, 2021, 38(4): 764-773. doi: 10.7507/1001-5515.202011011 Copy

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22.	颜昊霖, 安勇, 王宏飞, 等. 基于卷积神经网络的心电特征提取. 计算机工程与设计, 2017, 38(4): 1024-1028.
23.	Christov I I. Real time electrocardiogram QRS detection using combined adaptive threshold. Biomed Eng Online, 2004, 3(1): 1-9.
24.	李桥, Mark R G, Clifford G D, 等. 基于信号质量评估和卡尔曼滤波的心率估计算法. 中国医学物理学杂志, 2007, 24(6): 454-457.
25.	俞文彬, 谢志军, 叶宏武. 一种运动伪迹干扰下实时心率算法的研究与实现. 数据通信, 2015(5): 35-38.
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27.	王超. ECG 去噪和 R 波检测的算法研究. 合肥: 安徽大学, 2018.
28.	张清丽, 苏士美, 王猛. 基于改进小波变换的 QRS 特征提取算法研究. 郑州大学学报(理学版), 2017, 49(4): 100-103.
29.	ANSI/AAMI EC13: 2002. Cardiac monitors, heart rate meters, and alarms. Arlington: AAMI, 2002.
30.	周双, 杨学志, 金兢, 等. 采用自适应信号恢复算法的非接触式心率检测. 中国图象图形学报, 2019, 24(10): 1670-1682.
31.	Terbizan D J, Dolezal B A, Albano C. Validity of seven commercially available heart rate monitors. Meas Phys Educ Sci, 2002, 6(4): 243-247.

1. World Health Organization. World health statistics 2020: monitoring health for the SDGs, sustainable development goals. Geneva: World Health Organization, 2020: 1-92.
2. McGill H C, McMahan C A, Gidding S S. Preventing Heart Disease in the 21st Century: Implications of the Pathobiological Determinants of Atherosclerosis in Youth (PDAY) Study. Circulation, 2008, 117(9): 1216-1227.
3. Köhler B U, Hennig C, Orglmeister R. The principles of software QRS detection. IEEE Eng Med Biol Mag, 2002, 21(1): 42-57.
4. 李迪, 刘剑雄, 徐俊波, 等. 冠状动脉内心电图研究进展. 中国介入心脏病学杂志, 2016, 24(9): 534-538.
5. Liu C Y, Zhang X Y, Zhao L N, et al. Signal quality assessment and lightweight QRS detection for wearable ECG SmartVest system. IEEE Internet Things J, 2018, 6: 1363-1374.
6. 熊帆. 基于织物心电传感的“运动伪迹”机理及抑制算法研究. 成都: 电子科技大学, 2019.
7. Gruetzmann A, Hansen S, Müller J. Novel dry electrodes for ECG monitoring. Physiol Meas, 2007, 28(11): 1375-1390.
8. Baek J Y, An J H, Choi J M, et al. Flexible polymeric dry electrodes for the long-term monitoring of ECG. Sens Actuator A Phys, 2008, 143(2): 423-429.
9. Tamura T, Maeda Y, Sekine M, et al. Wearable photoplethysmographic sensors—Past and present. Electronics, 2014, 3(2): 282-302.
10. Lai P H, Kim I. Lightweight wrist photoplethysmography for heavy exercise: motion robust heart rate monitoring algorithm. Healthc Technol Lett, 2015, 2(1): 6-11.
11. Sharma P, Imtiaz S A, Rodriguez-Villegas E. An algorithm for heart rate extraction from acoustic recordings at the neck. IEEE Trans Biomed Eng, 2019, 66(1): 246-256.
12. Gambarotta N, Aletti F, Baselli G, et al. A review of methods for the signal quality assessment to improve reliability of heart rate and blood pressures derived parameters. Med Biol Eng Comput, 2016, 54(7): 1025-1035.
13. Nathan V, Jafari R. Particle Filtering and sensor fusion for robust heart rate monitoring using wearable sensors. IEEE J Biomed Health Inform, 2018, 22(6): 1834-1846.
14. Kannel W B, Kannel C, Paffenbarger R S Jr, et al. Heart rate and cardiovascular mortality: the Framingham Study. Am Heart J, 1987, 113(6): 1489-1494.
15. Sun F M, Yi C F, Li W N, et al. A wearable H-shirt for exercise ECG monitoring and individual lactate threshold computing. Comput Ind, 2017, 92-93: 1-11.
16. 梅思宇. 用于穿戴式心率监测仪的抗运动干扰技术研究. 西安: 西安电子科技大学, 2018.
17. 谢雨婷. 动态心电心率估计的多标签融合算法研究. 南京: 东南大学, 2019.
18. Thakor N V, Webster J G, Tompkins W J. Estimation of QRS complex power spectra for design of a QRS filter. IEEE Trans Biomed Eng, 1984, 31(11): 702-706.
19. 姚欢, 王剑钢. ECG 信号 QRS 波群检测算法的进展. 现代生物医学进展, 2012, 12(20): 3988-3991.
20. Pan J, Tompkins W J. A real-time QRS detection algorithm. IEEE Trans Biomed Eng, 1985, 32(3): 230-236.
21. 王文, 孙世双, 周勇. 基于小波变换的心电图 QRS 波群检测方法研究. 北京生物医学工程, 2002, 121(14): 241-243.
22. 颜昊霖, 安勇, 王宏飞, 等. 基于卷积神经网络的心电特征提取. 计算机工程与设计, 2017, 38(4): 1024-1028.
23. Christov I I. Real time electrocardiogram QRS detection using combined adaptive threshold. Biomed Eng Online, 2004, 3(1): 1-9.
24. 李桥, Mark R G, Clifford G D, 等. 基于信号质量评估和卡尔曼滤波的心率估计算法. 中国医学物理学杂志, 2007, 24(6): 454-457.
25. 俞文彬, 谢志军, 叶宏武. 一种运动伪迹干扰下实时心率算法的研究与实现. 数据通信, 2015(5): 35-38.
26. Chen G, Wang X, Wan W. An ECG R-wave detection algorithm based on adaptive threshold// 2015 International Conference on Smart and Sustainable City and Big Data (ICSSC). Shanghai: IET, 2015: 145-149.
27. 王超. ECG 去噪和 R 波检测的算法研究. 合肥: 安徽大学, 2018.
28. 张清丽, 苏士美, 王猛. 基于改进小波变换的 QRS 特征提取算法研究. 郑州大学学报(理学版), 2017, 49(4): 100-103.
29. ANSI/AAMI EC13: 2002. Cardiac monitors, heart rate meters, and alarms. Arlington: AAMI, 2002.
30. 周双, 杨学志, 金兢, 等. 采用自适应信号恢复算法的非接触式心率检测. 中国图象图形学报, 2019, 24(10): 1670-1682.
31. Terbizan D J, Dolezal B A, Albano C. Validity of seven commercially available heart rate monitors. Meas Phys Educ Sci, 2002, 6(4): 243-247.

Journal of Biomedical Engineering

A heart rate detection method for wearable electrocardiogram with the presence of motion interference

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