A Continuous Biomedical Signal Acquisition System Based on Compressed Sensing in Body Sensor Networks
A Continuous Biomedical Signal Acquisition System Based on Compressed Sensing in Body Sensor Networks
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DOI：
10.1109/tii.2013.2245334
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发表时间：
2013-08-01
影响因子：
12.3
通讯作者：
Wang, Xinheng
中科院分区：
文献类型：
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作者：
Li, Shancang;Xu, Li Da;Wang, Xinheng
新兴的压缩感知（CS）为身体传感器网络（BSN）中连续采集生物医学信号提供了相当大的希望，这使得节点能够采用比奈奎斯特低得多的采样率，同时仍然能够准确地重建信号。基于CS的BSN有望显著提高医疗质量，提高慢性病的预防、早期诊断和治疗能力。然而，现有的BSN仍然无法支持医疗保健中的长期监测，以及提供节能的低通信负担和廉价的方案。利用生物医学信号在传输域的稀疏性，提出了一种基于稀疏化模型的生物医学信号连续采集系统。信号的稀疏测量通过BSN无线传输到融合中心。同时，提出了一种加权分组稀疏重构算法，以精确重构融合中心处的信号。仿真结果表明，在BSN上随机采样时，所提出的分组稀疏算法具有较好的效率、较强的稳定性和鲁棒性。
The emerging compressed sensing (CS) holds considerable promise for continuously acquiring biomedical signals in body sensor networks (BSNs), which enables nodes to employ a much lower sampling rate than Nyquist while still able to accurately reconstruct signals. CS-based BSNs are expected to significantly enhance the quality of healthcare and improve the ability of prevention, early diagnosis, and treatment of chronic diseases. However, existing BSNs are still unable to support long-term monitoring in healthcare, as well as providing an energy-efficient low communication burden and inexpensive scheme. Capitalizing on the sparsity of biomedical signals in transfer domains, this paper develops a continuous biomedical signal acquisition system, which explores a sparsification model to find the sparse representation of biomedical signals. The sparsified measurements of signals are wirelessly transmitted to a fusion center through BSNs. Meanwhile, a weighted group sparse reconstruction algorithm is proposed to accurately reconstruct the signals at the fusion center. Simulation results show that, on random sampling over BSN, the proposed group sparse algorithm shows good efficiency, strong stability, and robustness.