Block-Sparse Compressive Sensing for High-Fidelity Recording of Photoplethysmogram

This paper presents a novel compressive sensing (CS) framework for photoplethysmogram (PPG) signal recording. Exploiting the concept of block sparsity in CS, the proposed framework trains a block-sparsifying dictionary for the PPG signal using the block K-SVD (BK-SVD) algorithm. Next, the block sparse Bayesian learning (BSBL) algorithm is employed to utilize the block-sparsity information and recover the PPG signal from its compressively sampled counterpart. Using different PPG datasets prerecorded from the fingertip of a healthy human volunteer under normal and post-exercise conditions, our results demonstrate that the proposed CS framework based on BK-SVD + BSBL can achieve signal-to-noise and distortion ratio (SNDR) values of >10dB for compression ratios as high as 10, outperforming the previous approaches for compressive sensing of PPG that do not utilize the block-sparsity information.