Wireless body area networks (WBANs) will become increasingly important in future communication systems, especially in the area of wearable health monitoring systems, such as telemonitoring systems for the collection of electrocardiogram (ECG) data/electroencephalogram (EEG) data via WBANs for e-health applications. However, wearable devices usually require limited power consumption to ensure long battery life. Fortunately, compressed sensing (CS) has been proven to use less energy than traditional transform-coding-based methods. Because the spatial and temporal data collected by a WBAN have some closely correlated structures in certain transform domains (e.g., the discrete cosine transform (DCT) domain), we exploit these structures to propose a new low-rank and joint-sparse (L&S) signal recovery algorithm for recovering ECG/EEG data in the framework of CS. Using a simultaneously L&S signal model, we employ a Bayesian learning treatment. This treatment incorporates an L&S-inducing prior over the data and appropriate hyperpriors over all hyperparameters and thereby yields an effective reconstruction of L&S data. Simulation results with synthetic and real ECG/EEG data demonstrate that the proposed algorithm is superior to other state-of-the-art recovery algorithms in terms of reconstruction performance with comparable computational complexity.

中文翻译：

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在 WBAN 中使用稀疏贝叶斯学习进行低秩和联合稀疏信号恢复

无线体域网 (WBAN) 在未来的通信系统中将变得越来越重要，尤其是在可穿戴健康监测系统领域，例如通过 WBAN 收集心电图 (ECG) 数据/脑电图 (EEG) 数据的远程监测系统，用于电子-健康应用。然而，可穿戴设备通常需要有限的功耗来确保较长的电池寿命。幸运的是，压缩感知 (CS) 已被证明比传统的基于变换编码的方法使用更少的能量。由于 WBAN 收集的空间和时间数据在某些变换域（例如离散余弦变换 (DCT) 域）中具有一些密切相关的结构，我们利用这些结构提出了一种新的低秩和联合稀疏（L& S) CS框架下ECG/EEG数据恢复的信号恢复算法。同时使用 L&S 信号模型，我们采用贝叶斯学习处理。这种处理结合了数据上的 L&S 诱导先验和所有超参数的适当超先验，从而产生了 L&S 数据的有效重建。使用合成和真实 ECG/EEG 数据的仿真结果表明，所提出的算法在具有可比计算复杂度的重建性能方面优于其他最先进的恢复算法。S-inducing 先验对数据和所有超参数的适当超先验，从而产生 L&S 数据的有效重建。使用合成和真实 ECG/EEG 数据的仿真结果表明，所提出的算法在具有可比计算复杂度的重建性能方面优于其他最先进的恢复算法。S-inducing 先验对数据和所有超参数的适当超先验，从而产生 L&S 数据的有效重建。使用合成和真实 ECG/EEG 数据的仿真结果表明，所提出的算法在具有可比计算复杂度的重建性能方面优于其他最先进的恢复算法。