Semi-Supervised Deep Blind Compressed Sensing for Analysis and Reconstruction of Biomedical Signals From Compressive Measurements

In this paper, the objective is to classify biomedical signals from their compressive measurements. The problem arises when compressed sensing (CS) is used for energy efficient acquisition and transmission of such signals for wireless body area network. After reconstruction, the signal is analyzed via certain machine learning techniques. This paper proposes to carry out joint reconstruction and analysis in a single framework; the reconstruction ability is obtained inherently from our formulation. We put forth a new technique called semi-supervised deep blind CS that combines the analytic power of deep learning with the reconstruction ability of CS. Experimental results on EEG classification show that the proposed technique excels over the state-of-the-art paradigm of CS reconstruction followed by deep learning classification.