An Energy-Efficient Compatible Method for Recovering Arterial Blood Pressure and Respiration Signals in WBANs

In Wireless Body Area Networks (WBANs), the sensor energy is limited. Due to dynamic and huge data exchange, sending data consumes the most sensor energy. The best solution to solve this problem is to use data compression methods. The Compressed Sensing (CS) method is among the popular methods for compressing data in WBANs. The problem with this method is does not work well when the data set is not sparse. In this paper, to solve this problem, two versions of Block Sparse Bayesian Learning (BSBL) Bound-Optimization (BSBL-BO), and Expectation-Maximization (BSBL-EM) are used to compress and recover the Arterial Blood Pressure systolic (ABPsys) and Respiration signals. These signals are adapted and reshaped to the BSBL environment as an input dataset and then compressed. The phi matrix is created compatible with ABPsys and Respiration signals and obtained 98% similarity with the original signal after restoration. According to the results, the similarity of ABPsys and Respiration signals after recovery by BSBL-BO is higher than the BSBL-EM method. BSBL-BO is faster at signal recovery than BSBL-EM. The amount of residual energy is compared between the two CS methods, DCT, as dictionary matrix in CS using the BSBL versions, and the DCT without the BSBL and DCT with BSBL performs better than alone DCT.