Compressed sensing-based method for electrocardiogram monitoring on wireless body sensor using binary matrix

被引:3
作者
State Key Laboratory of Transducer Technology, Institute of Electronics, Chinese Academy of Sciences, Beijing, China [1 ]
不详 [2 ]
机构
[1] State Key Laboratory of Transducer Technology, Institute of Electronics, Chinese Academy of Sciences, Beijing
[2] University of Chinese Academy of Sciences, Beijing
来源
Int. J. Wireless Mobile Comput. | / 2卷 / 114-121期
基金
中国国家自然科学基金;
关键词
Compressed sensing; Electrocardiogram monitoring; Measurement matrix; Wireless body sensor;
D O I
10.1504/IJWMC.2015.068623
中图分类号
学科分类号
摘要
Reducing the amount of wireless transmission data is beneficial for energy efficiency and lifetime of Wireless Body Sensor Networks (WBSN). Compressed Sensing (CS) approach can be applied to Electrocardiogram (ECG) data compression with its low execution complexity in node end. However, sensor node is often resource-constrained which means reduced memory space for measurement matrix storage. This paper presents a compressed sensing based method for real-time multi-node ECG monitoring. A minimum 512 bytes binary random measurement matrix is designed which is very suitable for resource constrained WBSN sensor node. Result of R-point extraction indicates that heart beat rate can be precisely derived from recovered signal. Finally, real-time measurement based on compressed sensing is accomplished by using the ECG node we developed. Simulation and experiment show that the proposed method of compressed sensing with measurement matrix can fulfil the requirements of WBSN enabled ECG node both in reconstruction quality and reconstruction time. Copyright © 2015 Inderscience Enterprises Ltd.
引用
收藏
页码:114 / 121
页数:7
相关论文
共 13 条
  • [1] Abdulghani A.M., Casson A.J., Rodriguez-Villegas E., Quantifying the feasibility of compressive sensing in portable electroencephalography systems, Proceedings of the International Conference on Foundations Augmented Cognition, Neuroergonomics and Operational Neuroscience, pp. 319-328, (2009)
  • [2] Balouchestani M., Raahemifar K., Krishnan S., Low sampling-rate approach for ECG signals with compressed sensing theory, Procedia Computer Science, 19, pp. 281-288, (2013)
  • [3] Candes E., Romberg J., Tao T., Robust uncertainty principles: Exact signal reconstruction from highly incomplete frequency information, IEEE Transactions on Information Theory, 52, 2, pp. 489-509, (2006)
  • [4] Chen S.S., Donoho D.L., Saunders M.A., Atomic decomposition by basis pursuit, SIAM Journal on Scientific Computing, 20, 1, pp. 33-61, (1998)
  • [5] Li F., Zhang H., Gao F., Li P., Location estimation for wireless sensor networks with attack tolerance, International Journal of Wireless and Mobile Computing, 6, 2, pp. 175-181, (2013)
  • [6] Mamaghanian H., Khaled N., Atienza D., Vandergheynst P., Compressed sensing for real-time energy-efficient ECG compression on wireless body sensor nodes, IEEE Transactions Biomedical Engineering, 58, 9, pp. 2456-2466, (2011)
  • [7] Moody G.B., Mark R.G., The impact of the MIT-BIH arrhythmia database, Engineering in Medicine and Biology Magazine, 20, 3, pp. 45-50, (2001)
  • [8] Needell D., Tropp J.A., Cosamp: Iterative signal recovery from incomplete and inaccurate samples, Communications of the ACM, 53, 12, pp. 93-100, (2010)
  • [9] Rana K., Zaveri M., Energy-efficient routing for wireless sensor network using genetic algorithm and particle swarm optimisation techniques, International Journal of Wireless and Mobile Computing, 6, 4, pp. 392-406, (2013)
  • [10] Tang S., Myers D., Yuan J., Modified SIS epidemic model for analysis of virus spread in wireless sensor networks, International Journal of Wireless and Mobile Computing, 6, 2, pp. 99-108, (2013)