东北大学学报（自然科学版）

东北大学学报:自然科学版 ›› 2019, Vol. 40 ›› Issue (8): 1087-1093.DOI: 10.12068/j.issn.1005-3026.2019.08.005

• 信息与控制 • 上一篇    下一篇

基于压缩感知的心电信号稀疏采样与重构算法

齐林， 邢家柱， 陈俊鑫， 张良钰   

  1. (东北大学 中荷生物医学与信息工程学院， 辽宁 沈阳110819)
  • 收稿日期:2018-07-21 修回日期:2018-07-21 出版日期:2019-08-15 发布日期:2019-09-04
  • 通讯作者: 齐林
  • 作者简介:齐林(1981-)，男，吉林长春人，东北大学副教授.
  • 基金资助:
    国家自然科学基金资助项目(61802055)； 辽宁省自然科学基金资助项目(20170520180)； 中央高校基本科研业务费专项资金资助项目(N171904009， N172008008)； 中国博士后科学基金资助项目(2018M630301).

Sparse Sampling and Reconstruction Algorithm of Electrocardiogram Signal in Compressed Sensing

QI Lin， XING Jia-zhu， CHEN Jun-xin， ZHANG Liang-yu   

  1. Sino-Dutch Biomedical & Information Engineering School， Northeastern University， Shenyang 110819， China.
  • Received:2018-07-21 Revised:2018-07-21 Online:2019-08-15 Published:2019-09-04
  • Contact: CHEN Jun-xin
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摘要： 实时心电监测的数据量过大，给系统的传输和存储带来很大压力.为降低采集端的功耗，达到既减轻采样复杂度又降低传输数据量的目的，使用压缩感知技术对心电信号进行压缩采样及重构.以信号重构时间和重构误差为关键指标，研究不同重构算法和小波基的性能表现.结果表明，当压缩率在30%以内时，基追踪作为信号重构算法的百分比均方根差小于4%，同时其重构耗时最短;当压缩率在70%以内时，子空间追踪的误差小于10%，且始终保持较低的重构耗时.最优小波基往往和具体压缩率有关.

关键词: 压缩感知, 心电信号, 采样和重构, 小波基

Abstract: Real-time electrocardiogram(ECG) monitoring will result in large data volume， which brings about severe pressure on the transmission and storage of the system. In order to reduce the computational complexity of data acquisition and the data volume in transmission， compressed sensing was used for the ECG signal. Taking time and error of signal reconstruction as critical indicators， the performance of different reconstruction algorithms and wavelet basis are comprehensively studied. The results demonstrate that when the compression ratio is within 30%， the percentage mean-square difference of the basis tracking algorithm is less than 4%， and the reconstruction takes the shortest time. When the compression ratio is within 70%， the error of subspace tracking algorithm is less than 10%， and the reconstruction time is always the lowest. However， the optimal wavelet basis is generally related to the specific compression ratio.

Key words: compressed sensing, electrocardiogram signal, acquisition and reconstruction, wavelet basis

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