Compressive Sensing Reconstruction Of Sparse Ecg Signal Via Orthogonal Matching Pursuit Reconstruction Algorithm

Abstract

The compressive sensing (CS) technique is a novel tool used to reconstruct signals using fewer samples, normally sparse in the transform domain, than those required by conventional imaging systems. However, the methods applied for signal reconstruction within the CS approach still present some problems in the implementation, mainly due to their intensive computational demand and high power consumption requirements. This paper evaluates the use of reconstruction based parallel processing architecture for the implementation of the Orthogonal Matching Pursuit (OMP) algorithm, one of the most efficient CS reconstruction algorithms developed so far. Speed architecture for the orthogonal matching pursuit (OMP) algorithm, which is the most frequently used to reconstruct compressively sensed signals. The proposed design offers a very high throughput and includes an innovative pipeline architecture and scheduling algorithm. Using electrocardiograms (ECGs) of heart signals as research data, an improved segmented OMP algorithm was developed to compress and reconstruct the signals. Finally, feature values were extracted from the reconstructed signals for identification and analysis.