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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- 文章访问数: 2173
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- 被引次数: 0
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)

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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