中国邮电高校学报(英文) ›› 2019, Vol. 26 ›› Issue (4): 62-69.doi: DOI: 10.19682/j.cnki.1005-8885.2019.1018

• Artificial Intelligence • 上一篇    下一篇

Improved ECG signal compression method for E-healthcare

Gao Miao, Yu Zhiguo, Li Qingqing, Wei Jinghe, Gu Xiaofeng   

  1. 1. Engineering Research Center of Internet of Things Technology Applications, Ministry of Education,
    Department of Electronic Engineering, Jiangnan University, Wuxi 214122, China
    2. China Electronic Technology Group Corporation No. 58 Research Institute, Wuxi 214035, China
  • 收稿日期:2018-08-28 修回日期:2019-04-17 出版日期:2019-08-31 发布日期:2019-10-29
  • 通讯作者: Corresponding author: Yu Zhiguo, E-mail: yuzhiguo@jiangnan.edu.cn E-mail:yuzhiguo@jiangnan.edu.cn
  • 作者简介:Corresponding author: Yu Zhiguo, E-mail: yuzhiguo@jiangnan.edu.cn
  • 基金资助:
    This work was supported by the Fundamental Research Funds for the Central Universities (JUSRP51510), the Postgraduate Research and Practice Innovation Program of Jiangsu Province (SJCX17_0510, SJCX18 _0647), and the China Scholarship Council (201706795031).

Improved ECG signal compression method for E-healthcare

Gao Miao, Yu Zhiguo, Li Qingqing, Wei Jinghe, Gu Xiaofeng   

  1. 1. Engineering Research Center of Internet of Things Technology Applications, Ministry of Education, Department of Electronic Engineering, Jiangnan University, Wuxi 214122, China 2. China Electronic Technology Group Corporation No. 58 Research Institute, Wuxi 214035, China
  • Received:2018-08-28 Revised:2019-04-17 Online:2019-08-31 Published:2019-10-29
  • Contact: Corresponding author: Yu Zhiguo, E-mail: yuzhiguo@jiangnan.edu.cn E-mail:yuzhiguo@jiangnan.edu.cn
  • About author:Corresponding author: Yu Zhiguo, E-mail: yuzhiguo@jiangnan.edu.cn
  • Supported by:
    This work was supported by the Fundamental Research Funds
    for the Central Universities (JUSRP51510), the Postgraduate
    Research and Practice Innovation Program of Jiangsu Province
    (SJCX17_0510, SJCX18 _0647), and the China Scholarship
    Council (201706795031).

摘要: For achieving a higher compression ratio (CR) in compression sensing, the time-sparse bio-signals, such as electrocardiograph (ECG), are generally directly filtered via a dynamic or fixed threshold, however, inevitably leading to the loss of critical diagnostic bio-information. We propose a compression scheme to reduce the transmitting loss. Instead of the directly utilizing the original ECG data, the residuals between original and synthetic ECG signals are applied as the input signal. We employ the dynamic model to guarantee the consistency between the synthetic ECG signals waves (P, Q, R, S, and T) and the originals. The feasibility of the proposed method is tested through operating on the recorded ECG signals from a healthy human. During the process of building simulation platform, the sparsity, percentage root mean difference (PRD) versus sampling frequency, and signals reconstruction algorithm are fully taken into account. Before compression, we set the threshold to filter the residual waves, in which utilizing the residuals as input data by setting the thresold as 0.01 mV and 0.08 mV resulted the amount reduction of the transmitting data by 18% and 81.2%, respectively. And the simulation results show that CR can reach 2.75 when the PRD value is less than 9%.

关键词: compressed sensing (CS), dynamical model, synthetic ECG, residuals

Abstract: For achieving a higher compression ratio (CR) in compression sensing, the time-sparse bio-signals, such as electrocardiograph (ECG), are generally directly filtered via a dynamic or fixed threshold, however, inevitably leading to the loss of critical diagnostic bio-information. We propose a compression scheme to reduce the transmitting loss. Instead of the directly utilizing the original ECG data, the residuals between original and synthetic ECG signals are applied as the input signal. We employ the dynamic model to guarantee the consistency between the synthetic ECG signals waves (P, Q, R, S, and T) and the originals. The feasibility of the proposed method is tested through operating on the recorded ECG signals from a healthy human. During the process of building simulation platform, the sparsity, percentage root mean difference (PRD) versus sampling frequency, and signals reconstruction algorithm are fully taken into account. Before compression, we set the threshold to filter the residual waves, in which utilizing the residuals as input data by setting the thresold as 0.01 mV and 0.08 mV resulted the amount reduction of the transmitting data by 18% and 81.2%, respectively. And the simulation results show that CR can reach 2.75 when the PRD value is less than 9%.

Key words: compressed sensing (CS), dynamical model, synthetic ECG, residuals

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