基于过完备字典的体域网压缩感知心电重构

彭向东; 张华; 刘继忠

doi:10.3724/SP.J.1004.2014.01421

基于过完备字典的体域网压缩感知心电重构

doi: 10.3724/SP.J.1004.2014.01421

1.
南昌大学机器人研究所南昌 330031;
2.
江西财经大学软件与通信工程学院南昌 330013

基金项目:

国家自然科学基金（61273282），江西省高等学校科技落地计划项目（KJLD13002），江西省科技计划项目（2011BB50030）资助

详细信息

作者简介:
彭向东南昌大学机电学院博士研究生.2007 年获华中科技大学硕士学位. 主要研究方向为服务机器人，体域网，压缩感知. E-mail：pxdfj@163.com

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出版历程
- 收稿日期: 2013-10-24
- 修回日期: 2014-02-17
- 刊出日期: 2014-07-20

ECG Reconstruction of Body Sensor Network Using Compressed Sensing Based on Overcomplete Dictionary

1.
Robot Institute, Nanchang University, Nanchang 330031;
2.
School of Software and Communication Engineering, Jiangxi University of Finance and Economics, Nanchang 330013

Funds:

Supported by National Natural Science Foundation of China (61273282), College Science and Technology Ground Plan Project of Jiangxi Province (KJLD13002), and Science and Technology Plan Projects of Jiangxi Province (2011BB50030)

摘要

摘要: 针对体域网远程监护中心对重构的心电信号（Electrocardiogram，ECG）精度要求高和体域网（Body sensor network，BSN）低功耗问题，提出基于过完备字典的体域网压缩感知心电重构方法. 该方法利用压缩感知理论，在传感节点端利用随机二进制矩阵对心电信号进行观测，观测值被传送至远程监护中心后，再利用基于K-SVD算法训练得到的过完备字典和块稀疏贝叶斯学习重构算法对心电信号进行重构. 仿真结果表明，当心电信号压缩率在70%～95%时，基于K-SVD过完备字典比基于离散余弦变换基的压缩感知心电重构信噪比高出5～22dB. 该方法具有信号重构精度高、功耗低和易于硬件实现的优点.
- 过完备字典 /
- 体域网 /
- 压缩感知 /
- 心电信号 /
- K-SVD
Abstract: Regarding to tackle the problem of demanding high accuracy reconstruction electrocardiogram (ECG) signal in a remote monitoring center of the body sensor network (BSN) and the low power problem of the body sensor network, this paper proposes a method of ECG reconstruction of body sensor network using compressed sensing based on overcomplete dictionary. The proposed method uses the compressed sensing theory and random binary matrices as the sensing matrix to measure the ECG signal on the sensor nodes. After the measured value is transmitted to the remote monitoring center, the overcomplete dictionary based on K-SVD algorithm training and the block sparse Bayesian learning reconstruction algorithm are used to reconstruct the ECG signal. Simulation results show that the SNR of the compressed sensing reconstruction ECG based on K-SVD overcomplete dictionary method is 5～22dB higher than that of the method using discrete cosine transform when the ECG signal compression rate is at 70%～95%．The method has the advantages of high accuracy of signal reconstruction, low power, and easy hardware implementation.
- Overcomplete dictionary /
- body sensor network (BSN) /
- compressed sensing /
- electrocardiogram (ECG) /
- K-SVD

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参考文献(20)

[1]	Li Ping, Wang Rui, Chu Zhen-Wei, Lv Xiao-Juan. Research development and prospect of remote ECG monitoring system. Contemporary Medicine, 2011, 17(22): 18-20(李萍, 王瑞, 鉏振伟, 吕晓娟. 远程心电监护系统研究的发展与展望. 当代医学, 2011, 17(22): 18-20)
[2]	Bao S D, Carmen C Y P, Shen L F, Zhang Y T. Authenticated symmetric-key establishment for medical body sensor networks. Journal of Electronics, 2007, 24(3): 421-427
[3]	Gong Ji-Bing, Wang Rui, Cui Li. Research advances and challenges of body sensor network. Journal of Computer Research and Development, 2010, 47(5): 737-753(宫继兵, 王睿, 崔莉. 体域网BSN的研究进展及面临的挑战. 计算机研究与发展, 2010, 47(5): 737-753)
[4]	Donoho D L. Compressed sensing. IEEE Transactions on Information Theory, 2006, 52(4): 1289-1306
[5]	Liu Fang, Wu Jiao, Yang Shu-Yuan, Jiao Li-Cheng. Research advances on structured compressive sensing. Acta Automatica Sinica, 2013, 39(12): 1980-1995(刘芳, 武娇, 杨淑媛, 焦李成. 结构化压缩感知研究进展. 自动化学报, 2013, 39(12): 1980-1995)
[6]	Candes E J, Wakin M. An introduction to compressive sampling. IEEE Signal Processing Magazine, 2008, 25(2): 21-30
[7]	Shi Guang-Ming, Liu Dan-Hua, Gao Da-Hua, Liu Zhe, Lin Jie, Wang Liang-Jun. Advances in theory and application of compressed sensing. Acta Sinica Electronica, 2009, 37(5): 1070-1081(石光明, 刘丹华, 高大化, 刘哲, 林杰, 王良君. 压缩感知理论及其研究进展. 电子学报, 2009, 37(5): 1070-1081)
[8]	Mamaghanian H, Khaled N, Atienza D, Vandergheynst P. Compressed sensing for real-time energy-efficient ECG compression on wireless body sensor nodes. IEEE Transactions on Biomedical Engineering, 2011, 58(9): 2456-2466
[9]	Zhang Z, Jung T P, Makeig S, Rao B D. Compressed sensing for energy-efficient wireless telemonitoring of noninvasive fetal ECG via block sparse Bayesian learning. IEEE Transactions on Biomedical Engineering, 2013, 60(2): 300-309
[10]	Mamaghanian H, Khaled N, Atienza D, Vandergheynst P. Design and exploration of low-power analog to information conversion based on compressed sensing. IEEE Journal on Emerging and Selected Topics in Circuits and Systems, 2012, 2(3): 493-501
[11]	Dixon A M R, Allstot E G, Gangopadhyay D, Allstot D J. Compressed sensing system considerations for ECG and EMG wireless biosensors. IEEE Transactions on Biomedical Circuits and Systems, 2012, 6(2): 156-166
[12]	Sun Lin-Hui, Yang Zhen, Ji Yun-Yun, Ye Lei. Reconstruction of compressed speech sensing based on overcomplete linear prediction dictionary. Chinese Journal of Scientific Instrument, 2012, 33(4): 743-749(孙林慧, 杨震, 季云云, 叶蕾. 基于过完备线性预测字典的压缩感知语音重构. 仪器仪表学报, 2012, 33(4): 743-749)
[13]	Doneva M, Bornert P, Eggers H, Stehning C, Sénégas J, Mertins A. Compressed sensing reconstruction for magnetic resonance parameter mapping. Magnetic Resonance in Medicine, 2010, 64(4): 1114-1120
[14]	Candes E J. The restricted isometry property and its implications for compressed sensing. Comptes Rendus Mathematique, 2008, 346(9-10): 589-592
[15]	Candes E J, Eldar Y C, Needell D, Randall P. Compressed sensing with coherent and redundant dictionaries. Applied and Computational Harmonic Analysis, 2011, 31(1): 59-73
[16]	Aharon M, Elad M, Bruckstein A. K-SVD: an algorithm for designing overcomplete dictionaries for sparse representation. IEEE Transactions on Signal Processing, 2006, 54(11): 4311-4322
[17]	Zhang Z, Rao B D. Extension of SBL algorithms for the recovery of block sparse signals with intra-block correlation. IEEE Transactions on Signal Processing, 2013, 61(8): 2009 -2015
[18]	Tropp J A, Gilbert A C. Signal recovery from random measurements via orthogonal matching pursuit. IEEE Transactions on Information Theory, 2007, 53(12): 4655-4666
[19]	Chen S S, Donoho D L, Saunders M A. Atomic decomposition by basis pursuit. SIAM Journal on Scientific Computing, 2001, 43(1): 129-159
[20]	Rajpoot P S, Wadhwani S, Wadhwani A K. ECG data compression based on principal component analysis. Current Research in Engineering, Science and Technology Journals, 2013, 1(4): 98-104

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基于过完备字典的体域网压缩感知心电重构

doi: 10.3724/SP.J.1004.2014.01421

作者简介:
彭向东南昌大学机电学院博士研究生.2007 年获华中科技大学硕士学位. 主要研究方向为服务机器人，体域网，压缩感知. E-mail：pxdfj@163.com

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ECG Reconstruction of Body Sensor Network Using Compressed Sensing Based on Overcomplete Dictionary

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基于过完备字典的体域网压缩感知心电重构

doi: 10.3724/SP.J.1004.2014.01421

作者简介: 彭向东 南昌大学机电学院博士研究生.2007 年获华中科技大学硕士学位. 主要研究方向为服务机器人，体域网，压缩感知. E-mail：pxdfj@163.com

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出版历程

ECG Reconstruction of Body Sensor Network Using Compressed Sensing Based on Overcomplete Dictionary

计量

出版历程

目录

作者简介:
彭向东南昌大学机电学院博士研究生.2007 年获华中科技大学硕士学位. 主要研究方向为服务机器人，体域网，压缩感知. E-mail：pxdfj@163.com