Robust reconstruction of compressively sensed ECG signals

This paper presents a robust reconstruction technique of electrocardiograph (ECG) signals in a compressed sensing based acquisition system, using custom complete and overcomplete dictionaries composed of real ECG patterns. Both signals and atoms are preprocessed segments of ECG recordings. We tested three types of projection matrices and found that the best reconstruction results are obtained when the projection matrix is the product of a random matrix with the transpose of the dictionary used for the sparse representation. Further improvements are obtained by reconstructing every signal multiple times from the same measurements, using different randomized dictionaries, and then averaging all the reconstructions.