A Subspace Projection-Based Joint Sparse Recovery Method for Structured Biomedical Signals
Sparse signal processing has shown a significant promise for the reconstruction of biomedical signals, which possess well-defined sparsity structures in an appropriate transform domain. In this paper, a reconstruction algorithm is proposed based on the multiple-measurement-vector model using the hidden sparsity and correlation structure of biomedical signals. This incorporates subspace filtering over the partial supports, initialized by the sparse Bayesian learning framework along with a maximum projection-based support-estimation technique. The proposed method outperforms the conventional algorithms with respect to correct support recovery rate and needs fewer measurements. The robustness is studied by employing real-world biomedical signals. The experimental results using multichannel fetal-electrocardiogram signals and respiratory signals, collected from the Physiobank database, show that the proposed technique is an improved method in terms of reconstruction quality and compression rate.