Low-rank and joint-sparse signal recovery using sparse Bayesian learning in a WBAN
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Low-rank and joint-sparse signal recovery using sparse Bayesian learning in a WBAN Yan-Bin Zhang1 · Long-Ting Huang2 · Yang-Qing Li1 · Ke-Sen He1 · Kai Zhang1 · Chang-Chuan Yin1 Received: 31 January 2020 / Revised: 28 July 2020 / Accepted: 6 August 2020 © Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract 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
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Chang-Chuan Yin [email protected] Yan-Bin Zhang [email protected] Long-Ting Huang [email protected] Yang-Qing Li [email protected] Ke-Sen He [email protected] Kai Zhang [email protected]
1
Beijing Laboratory of Advanced Information Networks, Beijing Key Laboratory of Network System Architecture and Convergence, Beijing University of Posts and Telecommunications, Beijing 100876, China
2
School of Information Engineering, Wuhan University of Technology, Wuhan 430205, China
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Multidimensional Systems and Signal Processing
algorithm is superior to other state-of-the-art recovery algorithms in terms of reconstruction performance with comparable computational complexity. Keywords Wireless body area network · Sparse Bayesian learning · Compressed sensing · Low-rank and Joint-sparse
1 Introduction As the next generation of wireless communication networks, representing enormous development in information and communication technology (ICT) (Astrin et al. 2009), emerges, wireless body area networks (WBANs) (Cao et al. 2009; Ravelomanantsoa et al. 2014; Singh et al. 2014; Negra et al. 2016) are expected to become increasingly important in future communication systems, especially in the area of wearable health monitoring systems. WBANs play an important role in supporting medical and healthcare services based on wearable health monitoring systems. Generally, a WBAN consists of many sensor nodes, each capable of sampling, processing, and communicating one or more vital signs (e.g., hear
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