Performance of compressive sensing for the reconstruction of different QRS pulses in ECG signals.

Abstract

Compressive sensing (CS) is studied for the acquisition of different QRS complexes in ECG signals. By applying sparse binary measurement matrix and by applying regularization for promoting temporal correlation, performance of CS techniques for the reconstruction of ECG segments containing different QRS complexes, such as, normal QRS, paced QRS, right bundle branch block beat, left bundle branch block beat, and ventricular flutter beat, has been studied. Simulation results demonstrate that performance of CS differs with the types of beats, and ECG segments with well-known beats can be reconstructed with signal-to-noise ratio (SNR) approximately equal to 25dB for the realization of CS systems with compression ratio as high as 95%; for lower compression ratio much higher SNR can be attained.