通过压缩感知进行压缩：用于多通道生理信号远程监测的低功耗框架
Compression via Compressive Sensing : A Low-Power Framework for the Telemonitoring of Multi-Channel Physiological Signals
摘要：
远程医疗和可穿戴设备可以远程提供个人医疗保健和必要的治疗。一个主要的挑战是通过无线网络传输大量的生物信号。有限的电池寿命要求低功耗的数据压缩器。压缩传感（CS）已被证明是一种低功耗的压缩机。在这项研究中，我们应用CS多通道生物信号的压缩。我们首先从块稀疏贝叶斯学习（BSBL）框架中开发了一种高效的CS算法。它是基于块稀疏模型和多个测量向量模型的组合。在真实胎儿心电图上的实验表明，该算法具有较高的逼真度和效率。通过硬件实现，与基于离散小波变换（DWT）的算法进行了比较，验证了该算法具有低功耗和占用较少计算资源的优点。
英文摘要：
Telehealth and wearable equipment can deliver personal healthcare and necessary treatment remotely. One major challenge is transmitting large amount of biosignals through wireless networks. The limited battery life calls for low-power data compressors. Compressive Sensing (CS) has proved to be a low-power compressor. In this study, we apply CS on the compression of multichannel biosignals. We firstly develop an efficient CS algorithm from the Block Sparse Bayesian Learning (BSBL) framework. It is based on a combination of the block sparse model and multiple measurement vector model. Experiments on real-life Fetal ECGs showed that the proposed algorithm has high fidelity and efficiency. Implemented in hardware, the proposed algorithm was compared to a Discrete Wavelet Transform (DWT) based algorithm, verifying the proposed one has low power consumption and occupies less computational resources.
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