Low-power Wireless Micro Ambulatory Electrocardiogram Node<sup>*</sup>_Journal of Biomedical Engineering

Authors：

CAIZhipeng ¹ , LUOKan ^2,1 ,  LIJianqing ¹

1. School of Instrument Science and Engineering, Southeast University, Nanjing 210096, China;
2. College of Information Science and Engineering, Fujian University of Technology, Fuzhou 350118, China;

Corresponding author：

LIJianqing, Email: ljq@seu.edu.cn

Keywords：

Body Sensor Network; electrocardiogram; R wave detection; low power

DOI：

10.7507/1001-5515.20160003

Video：

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Abstract Full text Figures/Tables Video References Cited by

Ambulatory electrocardiogram (ECG) monitoring can effectively reduce the risk and death rate of patients with cardiovascular diseases (CVDs). The Body Sensor Network (BSN) based ECG monitoring is a new and efficient method to protect the CVDs patients. To meet the challenges of miniaturization, low power and high signal quality of the node, we proposed a novel 50 mm×50 mm×10 mm, 30 g wireless ECG node, which includes the single-chip analog front-end AD8232, ultra-low power microprocessor MSP430F1611 and Bluetooth module HM-11. The ECG signal quality is guaranteed by the on-line digital filtering. The difference threshold algorithm results in accuracy of R-wave detection and heart rate. Experiments were carried out to test the node and the results showed that the proposed node reached the design target, and it has great potential in application of wireless ECG monitoring.

Citation： CAIZhipeng, LUOKan, LIJianqing. Low-power Wireless Micro Ambulatory Electrocardiogram Node^*. Journal of Biomedical Engineering, 2016, 33(1): 8-13. doi: 10.7507/1001-5515.20160003 Copy

1.	LUO Kan, LI Jianqing, WU Jianfeng. A dynamic compression scheme for Energy-Efficient Real-Time wireless electrocardiogram biosensors[J]. IEEE Trans Instrum Meas, 2014, 63(9): 2160-2169.
2.	CHEN Yiping, LI Liming, ZHANG Qiuli, et al. Use of drug treatment for secondary prevention of cardiovascular disease in urban and rural communities of China: China Kadoorie Biobank Study of 0.5 million people[J]. Int J Cardiol, 2014, 172(1): 88-95.
3.	HU Shengshou, KONG Lingzhi, GAO Runlin, et al. Outline of the report on cardiovascular disease in China, 2010[J]. Biomed Environ Sci, 2012, 25(3): 251-256.
4.	雷礼琴,朱婉红．24小时动态心电图在不明原因晕厥患者临床诊断中的应用价值分析[J]．实用心脑肺血管病杂志,2014,(6)：113-114.
5.	罗堪,李建清,王志刚,等．心电压缩感知恢复先验块稀疏贝叶斯学习算法[J]．仪器仪表学报,2014,(8)：1883-1889.
6.	MAMAGHANIAN H, KHALED N, ATIENZA D, et al. Compressed sensing for real-time energy-efficient ECG compression on wireless body sensor nodes[J]. IEEE Trans Biomed Eng, 2011, 58(9): 2456-2466.
7.	DILMAGHANI R S, BOBARSHAD H, GHAVAMI M, et al. Wireless sensor networks for monitoring physiological signals of multiple patients[J]. IEEE Trans Biomed Circuits Syst, 2011, 5(4): 347-356.
8.	PANTELOPOULOS A, BOURBAKIS N G. A survey on wearable sensor-based systems for health monitoring and prognosis[J]. IEEE Transactions on Systems Man and Cybernetics Part C-Applications and Reviews, 2010, 40(1): 1-12.
9.	LIN Chinteng, CHANG Kuancheng, LIN Chunling, et al. An intelligent telecardiology system using a wearable and wireless ECG to detect atrial fibrillation[J]. IEEE Trans Inf Technol Biomed, 2010, 14(3): 726-733.
10.	陈斌,秦丽华．小波阈值去噪在手持心电图机ECG信号中的应用[J]．信息系统工程,2010,(6)：72.
11.	NEMATI E, DEEN M J, MONDAL T. A wireless wearable ECG sensor for long-term applications[J]. IEEE Communications Magazine, 2012, 50(1): 36-43.
12.	AGRAWAL S, GUPTA A. Removal of baseline wander in ECG using the statistical properties of fractional Brownian motion[C]// 2013 IEEE International Conference on Electronics, Computing and Communication Technologies (CONECCT). Bangalore: 2013: 1-6.
13.	GOMEZ C, OLLER J, PARADELLS J. Overview and evaluation of bluetooth low energy: an emerging low-power wireless technology[J]. Sensors, 2012, 12(9): 11734-11753.
14.	NACRA M, SAMIR B, MOKHTAR A. Hardware-software preprocessing platform for high immunity ECG recording[C]// 2011 International Conference on Communications, Computing and Control Applications (CCCA). Hammamet: 2011: 1-4.
15.	CHRISTOV I I. Real time electrocardiogram QRS detection using combined adaptive threshold[J]. Biomed Eng Online, 2004, 3(1): 28.
16.	DI MARCO L Y, CHIARI L. A wavelet-based ECG delineation algorithm for 32-bit integer online processing[J]. Biomed Eng Online, 2011, 10(1): 23.
17.	KRASTEVA V, JEKOVA I. QRS template matching for recognition of ventricular ectopic beats[J]. Ann Biomed Eng, 2007, 35(12): 2065-2076.

1. LUO Kan, LI Jianqing, WU Jianfeng. A dynamic compression scheme for Energy-Efficient Real-Time wireless electrocardiogram biosensors[J]. IEEE Trans Instrum Meas, 2014, 63(9): 2160-2169.
2. CHEN Yiping, LI Liming, ZHANG Qiuli, et al. Use of drug treatment for secondary prevention of cardiovascular disease in urban and rural communities of China: China Kadoorie Biobank Study of 0.5 million people[J]. Int J Cardiol, 2014, 172(1): 88-95.
3. HU Shengshou, KONG Lingzhi, GAO Runlin, et al. Outline of the report on cardiovascular disease in China, 2010[J]. Biomed Environ Sci, 2012, 25(3): 251-256.
4. 雷礼琴,朱婉红．24小时动态心电图在不明原因晕厥患者临床诊断中的应用价值分析[J]．实用心脑肺血管病杂志,2014,(6)：113-114.
5. 罗堪,李建清,王志刚,等．心电压缩感知恢复先验块稀疏贝叶斯学习算法[J]．仪器仪表学报,2014,(8)：1883-1889.
6. MAMAGHANIAN H, KHALED N, ATIENZA D, et al. Compressed sensing for real-time energy-efficient ECG compression on wireless body sensor nodes[J]. IEEE Trans Biomed Eng, 2011, 58(9): 2456-2466.
7. DILMAGHANI R S, BOBARSHAD H, GHAVAMI M, et al. Wireless sensor networks for monitoring physiological signals of multiple patients[J]. IEEE Trans Biomed Circuits Syst, 2011, 5(4): 347-356.
8. PANTELOPOULOS A, BOURBAKIS N G. A survey on wearable sensor-based systems for health monitoring and prognosis[J]. IEEE Transactions on Systems Man and Cybernetics Part C-Applications and Reviews, 2010, 40(1): 1-12.
9. LIN Chinteng, CHANG Kuancheng, LIN Chunling, et al. An intelligent telecardiology system using a wearable and wireless ECG to detect atrial fibrillation[J]. IEEE Trans Inf Technol Biomed, 2010, 14(3): 726-733.
10. 陈斌,秦丽华．小波阈值去噪在手持心电图机ECG信号中的应用[J]．信息系统工程,2010,(6)：72.
11. NEMATI E, DEEN M J, MONDAL T. A wireless wearable ECG sensor for long-term applications[J]. IEEE Communications Magazine, 2012, 50(1): 36-43.
12. AGRAWAL S, GUPTA A. Removal of baseline wander in ECG using the statistical properties of fractional Brownian motion[C]// 2013 IEEE International Conference on Electronics, Computing and Communication Technologies (CONECCT). Bangalore: 2013: 1-6.
13. GOMEZ C, OLLER J, PARADELLS J. Overview and evaluation of bluetooth low energy: an emerging low-power wireless technology[J]. Sensors, 2012, 12(9): 11734-11753.
14. NACRA M, SAMIR B, MOKHTAR A. Hardware-software preprocessing platform for high immunity ECG recording[C]// 2011 International Conference on Communications, Computing and Control Applications (CCCA). Hammamet: 2011: 1-4.
15. CHRISTOV I I. Real time electrocardiogram QRS detection using combined adaptive threshold[J]. Biomed Eng Online, 2004, 3(1): 28.
16. DI MARCO L Y, CHIARI L. A wavelet-based ECG delineation algorithm for 32-bit integer online processing[J]. Biomed Eng Online, 2011, 10(1): 23.
17. KRASTEVA V, JEKOVA I. QRS template matching for recognition of ventricular ectopic beats[J]. Ann Biomed Eng, 2007, 35(12): 2065-2076.

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Low-power Wireless Micro Ambulatory Electrocardiogram Node^*