Compressive sensing of ECG signals based on mixed pseudonorm of the first- and second-order differences

An improved algorithm for the reconstruction of electrocardiogram signals in compressive sensing is proposed. The algorithm is based on the minimization of a mixed pseudonorm of first- and second-order differences of the signal. Locations of QRS segments are estimated using a technique based on signal derivatives and the Hilbert transform, and they are used to implement the mixed pseudonorm. Simulation results demonstrate that the proposed algorithm offers approximately 23.5%, 11.4%, 4.4%, and 2.1% improvement in signal-to-noise ratio for a compression ratio of 90%, 80%, 70%, and 60%, respectively, relative to several competitive state-of-the-art algorithms.