Unconstrained Accurate Beat-to-beat Heart Rate Extraction Based on Piezoelectric Ceramics Sensor

Zhen FANG; Zhongrui BAI; Xianxiang CHEN; Pan XIA; Zhengling HE; Rongjian ZHAO

doi:10.11999/JEIT200045

Volume 43 Issue 5

May 2021

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Article Navigation > Journal of Electronics & Information Technology > 2021 > 43(5): 1472-1479

Zhen FANG, Zhongrui BAI, Xianxiang CHEN, Pan XIA, Zhengling HE, Rongjian ZHAO. Unconstrained Accurate Beat-to-beat Heart Rate Extraction Based on Piezoelectric Ceramics Sensor[J]. Journal of Electronics & Information Technology, 2021, 43(5): 1472-1479. doi: 10.11999/JEIT200045

Citation:

Zhen FANG, Zhongrui BAI, Xianxiang CHEN, Pan XIA, Zhengling HE, Rongjian ZHAO. Unconstrained Accurate Beat-to-beat Heart Rate Extraction Based on Piezoelectric Ceramics Sensor[J]. Journal of Electronics & Information Technology, 2021, 43(5): 1472-1479. doi: 10.11999/JEIT200045

Citation:

Zhen FANG, Zhongrui BAI, Xianxiang CHEN, Pan XIA, Zhengling HE, Rongjian ZHAO. Unconstrained Accurate Beat-to-beat Heart Rate Extraction Based on Piezoelectric Ceramics Sensor[J]. Journal of Electronics & Information Technology, 2021, 43(5): 1472-1479. doi: 10.11999/JEIT200045

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Unconstrained Accurate Beat-to-beat Heart Rate Extraction Based on Piezoelectric Ceramics Sensor

doi: 10.11999/JEIT200045

Zhen FANG^{1, 2
,
,},
Zhongrui BAI^{1, 2},
Xianxiang CHEN¹,
Pan XIA^{1, 2},
Zhengling HE²,
Rongjian ZHAO¹

1.
Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China
2.
School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, China

Funds: The National Key Research and Development Project (2016YFC1304302, 2018YFC2001802, 2018YFC2001101)

Received Date: 2020-01-13
Rev Recd Date: 2020-11-30

Available Online: 2020-12-04

Publish Date: 2021-05-18

Abstract

Abstract

BallistoCardioGram (BCG) can be used for contactless detection of vital signs. In BCG’s beat-to-beat heart rate extraction, the lower mean absolute error is of great significance for accurately obtaining the user’s Heart Rate Variability (HRV) indicators. In order to solve the shortcomings in the accuracy of beat-to-beat heart rate calculation of most current methods, a BCG acquisition system based on piezoelectric ceramics sensor is designed in this paper. By adopting a suitable structure for the sensor‘s shell and a suitable sampling frequency, the sensitivity of the sensor and the time resolution of the BCG signal are increased. Through the analysis of BCG, the most suitable components in BCG is found to extract beat by beat cardiac cycle. At the same time, this paper proposes an adaptive template matching algorithm using AP clustering to extract accurately cardiac cycle information. Analysis of the data of 5741 heartbeats of 15 subjects shows that the average error of the beat-to-beat heartbeat cycle is 0.48%, the Mean Absolute Error (MAE) is 3.78 ms, and the heartbeat coverage is above 95%, which is better than other similar work.
- BallistoCardioGram (BCG),
- Piezoelectric ceramic sensor,
- Beat-to-beat heart rate,
- Mean Absolute Error (MAE)

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References(19)

References

[1]	JAVAID A Q, ASHOURI H, TRIDANDAPANI S, et al. Elucidating the hemodynamic origin of ballistocardiographic forces: Toward improved monitoring of cardiovascular health at home[J]. IEEE Journal of Translational Engineering in Health and Medicine, 2016, 4: 1900208. doi: 10.1109/jtehm.2016.2544752
[2]	BRUSER C, STADLTHANNER K, DE WAELE S, et al. Adaptive beat-to-beat heart rate estimation in ballistocardiograms[J]. IEEE Transactions on Information Technology in Biomedicine, 2011, 15(5): 778–786. doi: 10.1109/TITB.2011.2128337
[3]	SHIN J H, HWANG S H, CHANG M H, et al. Heart rate variability analysis using a ballistocardiogram during Valsalva manoeuvre and post exercise[J]. Physiological Measurement, 2011, 32(8): 1239–1264. doi: 10.1088/0967-3334/32/8/015
[4]	YAO Yang, SHIN S, MOUSAVI A, et al. Unobtrusive estimation of cardiovascular parameters with limb ballistocardiography[J]. Sensors, 2019, 19(13): 2922. doi: 10.3390/s19132922
[5]	SCARBOROUGH W R and TALBOT S A. Proposals for ballistocardiographic nomenclature and conventions: Revised and extended report of committee on ballistocardiographic terminology[J]. Circulation, 1956, 14(3): 435–450. doi: 10.1161/01.CIR.14.3.435
[6]	JAVAID A Q, WIENS A D, FESMIRE N F, et al. Quantifying and reducing posture-dependent distortion in ballistocardiogram measurements[J]. IEEE Journal of Biomedical and Health Informatics, 2015, 19(5): 1549–1556. doi: 10.1109/JBHI.2015.2441876
[7]	崔晓雪, 成忠, 顾晔. 高血压合并糖尿病患者血压变异性与心率变异性的相关性[J]. 中国动脉硬化杂志, 2018, 26(6): 617–620. doi: 10.3969/j.issn.1007-3949.2018.06.014 CUI Xiaoxue, CHENG Zhong, and GU Ye. Correlation between blood pressure variability and heart rate variability in patients with hypertension and diabetes mellitus[J]. Chinese Journal of Arteriosclerosis, 2018, 26(6): 617–620. doi: 10.3969/j.issn.1007-3949.2018.06.014
[8]	MALIK J, LO Y L, and WU H T. Sleep-wake classification via quantifying heart rate variability by convolutional neural network[J]. Physiological Measurement, 2018, 39(8): 085004. doi: 10.1088/1361-6579/aad5a9
[9]	KIM H G, CHEON E J, BAI D S, et al. Stress and heart rate variability: A meta-analysis and review of the literature[J]. Psychiatry Investigation, 2018, 15(3): 235–245. doi: 10.30773/pi.2017.08.17
[10]	赵荣建, 汤敏芳, 陈贤祥, 等. 基于光纤传感的生理参数监测系统研究[J]. 电子与信息学报, 2018, 40(9): 2182–2189. doi: 10.11999/JEIT170894 ZHAO Rongjian, TANG Minfang, CHEN Xianxiang, et al. Research of physiological monitoring system based on optical fiber sensor[J]. Journal of Electronics and Information Technology, 2018, 40(9): 2182–2189. doi: 10.11999/JEIT170894
[11]	KENRY, YEO J C, and LIM C T. Emerging flexible and wearable physical sensing platforms for healthcare and biomedical applications[J]. Microsystems & Nanoengineering, 2016, 2: 16043. doi: 10.1038/micronano.2016.43
[12]	TANG Shihao, LIU Huafeng, YAN Shitao, et al. A high-sensitivity MEMS gravimeter with a large dynamic range[J]. Microsystems & Nanoengineering, 2019, 5: 45. doi: 10.1038/s41378-019-0089-7
[13]	ZHAO Mingmin, YUE Shichao, KATABI D, et al. Learning sleep stages from radio signals: A conditional adversarial architecture[C]. The 34th International Conference on Machine Learning, Sydney, Australia, 2017: 4100–4109.
[14]	杨昭, 杨学志, 霍亮, 等. 抗运动干扰的人脸视频心率估计[J]. 电子与信息学报, 2018, 40(6): 1345–1352. doi: 10.11999/JEIT170824 YANG Zhao, YANG Xuezhi, HUO Liang, et al. Heart rate estimation from face videos against motion interference[J]. Journal of Electronics &Information Technology, 2018, 40(6): 1345–1352. doi: 10.11999/JEIT170824
[15]	JIAO Changzhe, SU Boyu, LYONS P, et al. Multiple instance dictionary learning for beat-to-beat heart rate monitoring from ballistocardiograms[J]. IEEE Transactions on Biomedical Engineering, 2018, 65(11): 2634–2648. doi: 10.1109/TBME.2018.2812602
[16]	NAGURA M, MITSUKURA Y, KISHIMOTO T, et al. An estimation of heart rate variability from ballistocardiogram measured with bed leg sensors[C]. 2018 IEEE International Conference on Industrial Technology (ICIT), Lyon, France, 2018: 2005–2009. doi: 10.1109/ICIT.2018.8352495.
[17]	梁帆, 孟晓风, 余旸. 基于二阶伏特拉级数模型的心脏运动信号快速最小二乘估计[J]. 电子与信息学报, 2013, 35(3): 639–644. doi: 10.3724/SP.J.1146.2012.00866 LIANG Fan, MENG Xiaofeng, and YU Yang. Second order volterra series model based fast least square method for heart motion prediction[J]. Journal of Electronics &Information Technology, 2013, 35(3): 639–644. doi: 10.3724/SP.J.1146.2012.00866
[18]	FREY B J and DUECK D. Clustering by passing messages between data points[J]. Science, 2007, 315(5814): 972–976. doi: 10.1126/science.1136800
[19]	PAN Jiapu and TOMPKINS W J. A real-time QRS detection algorithm[J]. IEEE Transactions on Biomedical Engineering, 1985, BME-32(3): 230–236. doi: 10.1109/TBME.1985.325532